A novel portable infrared imaging system based on uncooled focal plane array and programmable system-on-chip（SoC）was proposed.The latest Xilinx Zynq-7000 was used to integrate the main part of the system into a sing...A novel portable infrared imaging system based on uncooled focal plane array and programmable system-on-chip（SoC）was proposed.The latest Xilinx Zynq-7000 was used to integrate the main part of the system into a single SoC.Parallel arithmetic units and digital modules were implemented on the programmable logic（PL）of Zynq-7000 to decrease system size and ensure the real-time p nonuniformity correction,while programs running on the processing system（PS）of Zynq-7000 controlled the system work flow and provided human-machine interfaces using open-source software such as Linux and OpenCV.Meanwhile,industry standard advanced extendable interface（AXI）buses were adopted to encapsulating standardized IP cores and build high speed data exchange bridges between units within Zynq-7000.Test results indicate that the image quality and real-time performance of the system can meet application requirements.And it provided a more flexible and extendable solution for evaluating and deploying infrared image enhancement and nonuniformity correction algorithms.展开更多
A key limitation in the observation of instruments used in operations and heart sutures during a procedure is the scattering and absorption during optical imaging in the presence of blood.Therefore,we propose a novel ...A key limitation in the observation of instruments used in operations and heart sutures during a procedure is the scattering and absorption during optical imaging in the presence of blood.Therefore,we propose a novel real-time fiber-optic infrared imaging system simultaneously capturing a flexible wide field of view(FOV) and large depth of field infrared image in real time.The assessment criteria for imaging quality of the objective and coupling lens have been optimized and evaluated.Furthermore,the feasibility of manufacturing and assembly has been demonstrated with tolerance sensitivity and the Monte Carlo analysis.The simulated results show that the optical system can achieve a large working distance of 8 to25 mm,a wide FOV of 120°,and the relative illuminance is over 0.98 in the overall FOV.To achieve high imaging quality in the proposed system,the modulation transfer function is over 0.661 at 16.7 Ip/mm for a 320×256 short wavelength infrared camera sensor with a pixel size of 30 μm.展开更多
In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
Real-time polarization medium-wave infrared(MIR)optical imaging systems enable the acquisition of infrared and polarization information for a target.At present,real-time polarization MIR devices face the following pro...Real-time polarization medium-wave infrared(MIR)optical imaging systems enable the acquisition of infrared and polarization information for a target.At present,real-time polarization MIR devices face the following problems:poor real-time performance,low transmission and high requirements for fabrication and integration.Herein,we aim to improve the performance of real-time polarization imaging systems in the MIR waveband and solve the above-mentioned defects.Therefore,we propose a MIR polarization imaging system to achieve real-time polarization-modulated imaging with high transmission as well as improved performance based on a pixel-wise metasurface micro-polarization array(PMMPA).The PMMPA element comprises several linear polarization(LP)filters with different polarization angles.The optimization results demonstrate that the transmittance of the center field of view for the LP filters is up to 77%at a wavelength of4.0μm and an extinction ratio of 88 d B.In addition,a near-diffraction-limited real-time MIR imaging optical system is designed with a field of view of 5°and an F-number of 2.The simulation results show that an MIR polarization imaging system with excellent real-time performance and high transmission is achieved by using the optimized PMMPA element.Therefore,the method is compatible with the available optical system design technologies and provides a way to realize real-time polarization imaging in MIR wavebands.展开更多
Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.H...Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.However,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition algorithm.Therefore,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at night.This approach uses a new night gait network(NGaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at night.First,the TIR image is represented via personal movements and enhanced body skeleton segments.Then,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′angles.These features are trained to identify the most discriminating gait patterns that indicate a change in human identity.To verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom website.The proposed thermal infrared imaging night gait recognition(TIRNGaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNGaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.展开更多
Based on an avalanche photodiode（ APD） detecting array working in Geiger mode（ GM-APD）, a high-performance infrared sensor readout integrated circuit（ ROIC） used for infrared 3D（ three-dimensional） imaging is ...Based on an avalanche photodiode（ APD） detecting array working in Geiger mode（ GM-APD）, a high-performance infrared sensor readout integrated circuit（ ROIC） used for infrared 3D（ three-dimensional） imaging is proposed. The system mainly consists of three functional modules, including active quenching circuit（ AQC）, time-to-digital converter（ TDC） circuit and other timing controller circuit. Each AQC and TDC circuit together constitutes the pixel circuit. Under the cooperation with other modules, the current signal generated by the GM-APD sensor is detected by the AQC, and the photon time-of-flight（ TOF） is measured and converted to a digital signal output to achieve a better noise suppression and a higher detection sensitivity by the TDC. The ROIC circuit is fabricated by the CSMC 0. 5 μm standard CMOS technology. The array size is 8 × 8, and the center distance of two adjacent cells is 100μm. The measurement results of the chip showthat the performance of the circuit is good, and the chip can achieve 1 ns time resolution with a 250 MHz reference clock, and the circuit can be used in the array structure of the infrared detection system or focal plane array（ FPA）.展开更多
The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information ...The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2 is described. The result shows that this is a high efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.展开更多
Background： Despite its variety of potential applications, the wide implementation of infrared technology in cattle production faces technical, environmental and biological challenges similar to other indicators of m...Background： Despite its variety of potential applications, the wide implementation of infrared technology in cattle production faces technical, environmental and biological challenges similar to other indicators of metabolic state. Nine trials, divided into three classes （technological, environmental and biological factors） were conducted to illustrate the influence of these factors on body surface temperature assessed through infrared imaging. Results： Evaluation of technological factors indicated the following： measurements of body temperatures were strongly repeatable when taken within ]0 s; appropriateness of differing infrared camera technologies was influenced by distance to the target; and results were consistent when analysis of thermographs was compared between judges. Evaluation of environmental factors illustrated that wind and debris caused decreases in body surface temperatures without affecting metabolic rate; additionally, body surface temperature increased due to sunlight but returned to baseline values within minutes of shade exposure. Examination/investigation/exploration of animal factors demonstrated that exercise caused an increase in body surface temperature and metabolic rate. Administration of sedative and anti-sedative caused changes on body surface temperature and metabolic rate, and during late pregnancy a foetal thermal imprint was visible through abdominal infrared imaging. Conclusion： The above factors should be considered in order to standardize operational procedures for taking thermographs, thereby optimizing the use of such technology in cattle operations.展开更多
Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.It could be utilized to visualize the sp...Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.It could be utilized to visualize the spatial distribution of the ingredients in a sample.The data acquired using NIR CI instrument are hyperspectral data cube(hypercube)containing thousands of spectra.Chemometric methodologies are necessary to transform spectral information into chemical information.Partial least squares(PLS)method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations.A series of samples which consisted of different CPM concentrations(w/w)were compressed and hypercube data were measured.The spectra extracted from the hypercube were used to establish the PLS model of CPM.The results of the model were R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.展开更多
Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoart...Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoarthritic articular cartilage in a canine model.Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament(ACL)transection in one knee.Cartilage specimens were sectioned into 10μm thickness for FTIRI.A PLS-DA model was developed after spectral pre-processing.All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%.Prior to FDA,principal component analysis(PCA)was performed to decompose the IR spectral matrix into informative princi pal component matrices.Based on the different discriminant mechanism,the discriminant accuracy(96%)of PCA-FDA with high convenience was higher than that of PLS-DA.No healthy cartilage sample was mis assigned by these two methods.The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and,especially,FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level.The results of the study would be helpful for better understanding the pathology of osteoarthritics.展开更多
Fourier transform infrared imaging(FTIRI)was used to examine the depth-dependent content variations of macromolcular components,ollagen and protooglycan(PG),in osteoarthritic and healthy cartilages.Dried 6 pmm thick s...Fourier transform infrared imaging(FTIRI)was used to examine the depth-dependent content variations of macromolcular components,ollagen and protooglycan(PG),in osteoarthritic and healthy cartilages.Dried 6 pmm thick sections of canine knee cartilages were imaged at 6.25 pμrm pixel-size in FTIRI.By analyzing the infrared(IR)images and spectra,the depth dependence of characteristic band(sugar)intensity of PG show obvious difference bet ween the cartilage sections of(OA)and bealth.The result confimns that PG content decreases in the ostcoarthritic cartilage.However,no clear change occurs to collagen,suggesting that the OA influences little on the collagen content at early stage of OA.This observation will be helpful to further understand PG loss associated with pathological conditions in OA,and demonstrates that FIIRI has the po-tential to become an important analytical tool to identify early clinical signs of tissue degna-dation,such as PG loss even collagen disruption.展开更多
Near infrared(NIR)fluorescence imaging guided photodynamic therapy(PDT)is a technique which has been developed in many clinical trials due to its advantage of real-time optical monitoring,specific spatiotemporal selec...Near infrared(NIR)fluorescence imaging guided photodynamic therapy(PDT)is a technique which has been developed in many clinical trials due to its advantage of real-time optical monitoring,specific spatiotemporal selectivity,and minimal invasiveness.For this,photosensitizers with NIR fluorescence emission and high^(1)O_(2)generation quantum yield are highly desirable.Herein,we designed and synthesized a"donor-acceptor"(D-A)structured semiconductor polymer(SP),which was then wrapped with an amphiphilic compound(Pluronic■F127)to prepare water-soluble nanoparticles(F-SP NPs).The obtained F-SP NPs exhibit good water solubility,excellent particle size stability,strong absorbance at deep red region,and strong NIR fluorescent emission characteristics.The maximal mass extinction coe±cient and fluorescence quantum yield of these F-SPs were calculated to be 21.7 L/(g·cm)and 6.5%,respectively.Moreover,the^(1)O_(2)quantum yield of 89%for F-SP NPs has been achieved under 635 nm laser irradiation,which is higher than Methylene Blue,Ce6,and PpIX.The outstanding properties of these F-SP NPs originate from their unique D-A molecular characteristic.This work should help guide the design of novel semiconductor polymer for NIR fluorescent imaging guided PDT applications.展开更多
A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The ne...A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.展开更多
Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means...Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.展开更多
An infrared imaging bolometer diagnostic has been upgraded recently to be adapted for the complications of the signal-to-noise ratio arising from the low level of plasma radiation and high reflectivity of low energy p...An infrared imaging bolometer diagnostic has been upgraded recently to be adapted for the complications of the signal-to-noise ratio arising from the low level of plasma radiation and high reflectivity of low energy photon（〈6.2 eV）.It utilizes a platinum foil,blackened on both sides with graphite spray,as the bolometer detector.The advantage of the blackened foil is the light absorption extending into the infrared.After a careful calibration of the foil,the incident power density distribution on the foil is determined by solving the heat diffusion equation with a numerical technique.The local plasma radiated power density is reconstructed with a minimum fisher information regularization method by assuming plasma emission toroidal symmetry.Comparisons of the results and the profiles measured by an ordinary bolometric detector demonstrate that this method is good enough to provide the plasma radiated power pattern.The typical plasma radiated power density distribution before and after high mode（H-mode） transition is firstly reconstructed with the infrared imaging bolometer.Moreover,during supersonic molecular beam injection（SMBI）,an enhanced radiation region is observed at the edge of the plasma.展开更多
Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of f...Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.展开更多
Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laborat...Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.展开更多
The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus co...The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.展开更多
In behaviour recognition, the development of the DL （Deep Learning） method introduced massive improvements in the field of artificial intelligence, where DL represents an upgrade of the present ANN （artificial neur...In behaviour recognition, the development of the DL （Deep Learning） method introduced massive improvements in the field of artificial intelligence, where DL represents an upgrade of the present ANN （artificial neural network） architecture. Deep Learning as a comprehensive new field of artificial intelligence completely covers the neural networks architecture that is devised to carry out certain forms of identification, such as behaviour, forms of things, trends, similarities in complex forms, etc. Regarding thermography in energy, the cases used to illustrate this are photographs of active energy components in the plant. Failures that are seen with thermography cannot be recognized by other methods. However, an expert needs to do segmentation of focusing and classification of failures. The need for daily sampling and expert work is growing. With the DL method, it can be done in real time any time. One of the popular network architectures for using DL in image analysis is the recognition algorithm--CNN （convolution neural network）. Traditional artificial intelligence methods require determining factors and computations, leading to training algorithm. Machine learning has important features as welt as the right weight to make decisions about new input data. This work presents DL as a flexible and adaptive method for the analysis of thermal images of energy facilities, as well as a tool used for the construction and implementation of an efficient fault analysis on the 10/0.4 kV service transformer.展开更多
Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this st...Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.展开更多
文摘A novel portable infrared imaging system based on uncooled focal plane array and programmable system-on-chip（SoC）was proposed.The latest Xilinx Zynq-7000 was used to integrate the main part of the system into a single SoC.Parallel arithmetic units and digital modules were implemented on the programmable logic（PL）of Zynq-7000 to decrease system size and ensure the real-time p nonuniformity correction,while programs running on the processing system（PS）of Zynq-7000 controlled the system work flow and provided human-machine interfaces using open-source software such as Linux and OpenCV.Meanwhile,industry standard advanced extendable interface（AXI）buses were adopted to encapsulating standardized IP cores and build high speed data exchange bridges between units within Zynq-7000.Test results indicate that the image quality and real-time performance of the system can meet application requirements.And it provided a more flexible and extendable solution for evaluating and deploying infrared image enhancement and nonuniformity correction algorithms.
基金supported by the Shanghai Science and Technology Committee Innovation Grant (No. 19ZR1404600)the National Natural Science Foundation of China (No. 52075100)
文摘A key limitation in the observation of instruments used in operations and heart sutures during a procedure is the scattering and absorption during optical imaging in the presence of blood.Therefore,we propose a novel real-time fiber-optic infrared imaging system simultaneously capturing a flexible wide field of view(FOV) and large depth of field infrared image in real time.The assessment criteria for imaging quality of the objective and coupling lens have been optimized and evaluated.Furthermore,the feasibility of manufacturing and assembly has been demonstrated with tolerance sensitivity and the Monte Carlo analysis.The simulated results show that the optical system can achieve a large working distance of 8 to25 mm,a wide FOV of 120°,and the relative illuminance is over 0.98 in the overall FOV.To achieve high imaging quality in the proposed system,the modulation transfer function is over 0.661 at 16.7 Ip/mm for a 320×256 short wavelength infrared camera sensor with a pixel size of 30 μm.
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
基金Project supported by the National Key R&D Program of China(Grant No.SKLA02020001A05)。
文摘Real-time polarization medium-wave infrared(MIR)optical imaging systems enable the acquisition of infrared and polarization information for a target.At present,real-time polarization MIR devices face the following problems:poor real-time performance,low transmission and high requirements for fabrication and integration.Herein,we aim to improve the performance of real-time polarization imaging systems in the MIR waveband and solve the above-mentioned defects.Therefore,we propose a MIR polarization imaging system to achieve real-time polarization-modulated imaging with high transmission as well as improved performance based on a pixel-wise metasurface micro-polarization array(PMMPA).The PMMPA element comprises several linear polarization(LP)filters with different polarization angles.The optimization results demonstrate that the transmittance of the center field of view for the LP filters is up to 77%at a wavelength of4.0μm and an extinction ratio of 88 d B.In addition,a near-diffraction-limited real-time MIR imaging optical system is designed with a field of view of 5°and an F-number of 2.The simulation results show that an MIR polarization imaging system with excellent real-time performance and high transmission is achieved by using the optimized PMMPA element.Therefore,the method is compatible with the available optical system design technologies and provides a way to realize real-time polarization imaging in MIR wavebands.
文摘Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.However,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition algorithm.Therefore,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at night.This approach uses a new night gait network(NGaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at night.First,the TIR image is represented via personal movements and enhanced body skeleton segments.Then,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′angles.These features are trained to identify the most discriminating gait patterns that indicate a change in human identity.To verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom website.The proposed thermal infrared imaging night gait recognition(TIRNGaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNGaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.
基金The Natural Science Foundation of Jiangsu Province(No.BK2012559)Qing Lan Project of Jiangsu Province
文摘Based on an avalanche photodiode（ APD） detecting array working in Geiger mode（ GM-APD）, a high-performance infrared sensor readout integrated circuit（ ROIC） used for infrared 3D（ three-dimensional） imaging is proposed. The system mainly consists of three functional modules, including active quenching circuit（ AQC）, time-to-digital converter（ TDC） circuit and other timing controller circuit. Each AQC and TDC circuit together constitutes the pixel circuit. Under the cooperation with other modules, the current signal generated by the GM-APD sensor is detected by the AQC, and the photon time-of-flight（ TOF） is measured and converted to a digital signal output to achieve a better noise suppression and a higher detection sensitivity by the TDC. The ROIC circuit is fabricated by the CSMC 0. 5 μm standard CMOS technology. The array size is 8 × 8, and the center distance of two adjacent cells is 100μm. The measurement results of the chip showthat the performance of the circuit is good, and the chip can achieve 1 ns time resolution with a 250 MHz reference clock, and the circuit can be used in the array structure of the infrared detection system or focal plane array（ FPA）.
文摘The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2 is described. The result shows that this is a high efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.
基金the Beef Producers of Ontario,Ontario Ministry of Agriculture and Rural Affairs,Beef Cattle Research Council and Agri-Food Canada for financial support
文摘Background： Despite its variety of potential applications, the wide implementation of infrared technology in cattle production faces technical, environmental and biological challenges similar to other indicators of metabolic state. Nine trials, divided into three classes （technological, environmental and biological factors） were conducted to illustrate the influence of these factors on body surface temperature assessed through infrared imaging. Results： Evaluation of technological factors indicated the following： measurements of body temperatures were strongly repeatable when taken within ]0 s; appropriateness of differing infrared camera technologies was influenced by distance to the target; and results were consistent when analysis of thermographs was compared between judges. Evaluation of environmental factors illustrated that wind and debris caused decreases in body surface temperatures without affecting metabolic rate; additionally, body surface temperature increased due to sunlight but returned to baseline values within minutes of shade exposure. Examination/investigation/exploration of animal factors demonstrated that exercise caused an increase in body surface temperature and metabolic rate. Administration of sedative and anti-sedative caused changes on body surface temperature and metabolic rate, and during late pregnancy a foetal thermal imprint was visible through abdominal infrared imaging. Conclusion： The above factors should be considered in order to standardize operational procedures for taking thermographs, thereby optimizing the use of such technology in cattle operations.
基金supported from Beijing Municipal Government for the university a±liated with the Party Central Committee(Prof.Shi)National Natural Science Foundation of China(81303218)+1 种基金Doctoral Fund of Ministry of Education of China(20130013120006)Special Fund of Beijing University of Chinese Medicine(Manfei Xu).
文摘Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.It could be utilized to visualize the spatial distribution of the ingredients in a sample.The data acquired using NIR CI instrument are hyperspectral data cube(hypercube)containing thousands of spectra.Chemometric methodologies are necessary to transform spectral information into chemical information.Partial least squares(PLS)method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations.A series of samples which consisted of different CPM concentrations(w/w)were compressed and hypercube data were measured.The spectra extracted from the hypercube were used to establish the PLS model of CPM.The results of the model were R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.
基金the National Natural Science Foundation of China for the grant of 61378087Natural Science Foundation of Jiangsu Province(BK20151478)+1 种基金Zhi-Hua Mao is grateful to the Open Funds for Graduate Innovation Lab of Nanjing University of Aeronautics and Astronautics(kfjj20150309)and Fundamental Research Funds for the Central Universities.The raw data acquisition in FTIRI was mostly carried out in the lab of Professor Yang Xia at Oakland University(Rochester,Michigan,USA).Professor Xia was supported by an NIH grant R01-AR052353 during the time of the data acquisition.
文摘Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoarthritic articular cartilage in a canine model.Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament(ACL)transection in one knee.Cartilage specimens were sectioned into 10μm thickness for FTIRI.A PLS-DA model was developed after spectral pre-processing.All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%.Prior to FDA,principal component analysis(PCA)was performed to decompose the IR spectral matrix into informative princi pal component matrices.Based on the different discriminant mechanism,the discriminant accuracy(96%)of PCA-FDA with high convenience was higher than that of PLS-DA.No healthy cartilage sample was mis assigned by these two methods.The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and,especially,FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level.The results of the study would be helpful for better understanding the pathology of osteoarthritics.
基金The authors are grateful to the National Institutes of Health in U.S.A.for the R01 grants(AR 045172,AR 052353)to Yang Xia.
文摘Fourier transform infrared imaging(FTIRI)was used to examine the depth-dependent content variations of macromolcular components,ollagen and protooglycan(PG),in osteoarthritic and healthy cartilages.Dried 6 pmm thick sections of canine knee cartilages were imaged at 6.25 pμrm pixel-size in FTIRI.By analyzing the infrared(IR)images and spectra,the depth dependence of characteristic band(sugar)intensity of PG show obvious difference bet ween the cartilage sections of(OA)and bealth.The result confimns that PG content decreases in the ostcoarthritic cartilage.However,no clear change occurs to collagen,suggesting that the OA influences little on the collagen content at early stage of OA.This observation will be helpful to further understand PG loss associated with pathological conditions in OA,and demonstrates that FIIRI has the po-tential to become an important analytical tool to identify early clinical signs of tissue degna-dation,such as PG loss even collagen disruption.
基金This work was supported by National Natural Science Foundation of China(Nos.61805287 and 62175262)The Open Fund of the State Key Laboratory of Luminescent Materials and Devices(South China University of Technology,No.2021-skllmd-10)+1 种基金The Open Sharing Fund for Large-scale Instruments and Equipment of Central South University(CSUZC202218),Fundamental Research Funds for the Central South Universities(Nos.2020CX021,2020zzts387,and 2020zzts404)Key R&D plan of Hunan Province(No.2022SK2101).
文摘Near infrared(NIR)fluorescence imaging guided photodynamic therapy(PDT)is a technique which has been developed in many clinical trials due to its advantage of real-time optical monitoring,specific spatiotemporal selectivity,and minimal invasiveness.For this,photosensitizers with NIR fluorescence emission and high^(1)O_(2)generation quantum yield are highly desirable.Herein,we designed and synthesized a"donor-acceptor"(D-A)structured semiconductor polymer(SP),which was then wrapped with an amphiphilic compound(Pluronic■F127)to prepare water-soluble nanoparticles(F-SP NPs).The obtained F-SP NPs exhibit good water solubility,excellent particle size stability,strong absorbance at deep red region,and strong NIR fluorescent emission characteristics.The maximal mass extinction coe±cient and fluorescence quantum yield of these F-SPs were calculated to be 21.7 L/(g·cm)and 6.5%,respectively.Moreover,the^(1)O_(2)quantum yield of 89%for F-SP NPs has been achieved under 635 nm laser irradiation,which is higher than Methylene Blue,Ce6,and PpIX.The outstanding properties of these F-SP NPs originate from their unique D-A molecular characteristic.This work should help guide the design of novel semiconductor polymer for NIR fluorescent imaging guided PDT applications.
文摘A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.
文摘Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.
基金supported by National Natural Science Foundation of China(Nos.10805016 and 11175061)the Chinese National Fusion Projectfor ITER(No.2014GB109001)
文摘An infrared imaging bolometer diagnostic has been upgraded recently to be adapted for the complications of the signal-to-noise ratio arising from the low level of plasma radiation and high reflectivity of low energy photon（〈6.2 eV）.It utilizes a platinum foil,blackened on both sides with graphite spray,as the bolometer detector.The advantage of the blackened foil is the light absorption extending into the infrared.After a careful calibration of the foil,the incident power density distribution on the foil is determined by solving the heat diffusion equation with a numerical technique.The local plasma radiated power density is reconstructed with a minimum fisher information regularization method by assuming plasma emission toroidal symmetry.Comparisons of the results and the profiles measured by an ordinary bolometric detector demonstrate that this method is good enough to provide the plasma radiated power pattern.The typical plasma radiated power density distribution before and after high mode（H-mode） transition is firstly reconstructed with the infrared imaging bolometer.Moreover,during supersonic molecular beam injection（SMBI）,an enhanced radiation region is observed at the edge of the plasma.
基金supported by the Fundamental Research Funds for the Central Universities(x2021-JYB-XJSJJ-032)Beijing Municipal Commission of Education,Double First-class,High-caliber Talents Grant(1000041510156)。
文摘Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.
基金supported partially by the USDA-ARS Research Project#6054-44000-080-00D.
文摘Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.
基金the National Natural Science Foundation of China(No.31772062)Gannan Camellia Industry Development and Innovative Center Open Fund(Grant No.YK201610).
文摘The growth characteristics of Aspergillus parasitic us incubated on two culture media were ex-amined using shortwave infrared(SWIR,1000-2500 nm)hyperspectral imaging(HSI)in this work.HSI images of the A.parasiticus colonies growing on rose bengal medium(RBM)and maize agar medium(MAM)were recorded daily for 6 days.The growth phases of A.parasiticus were indicated through the pixel number and average spectra of colonies.On score plot of the first principal component(PC1)and PC2,four growth zones with varying mycelium densities were identified.Eight characteristic wavelengths(1095,1145,1195,1279,1442,1655,1834 and 1929 nm)were selected from PC1 loading,average spectra of each colony as well as each growth zone.F urthermore,support vector machine(S VM)classifier based on the eight wavelengths was built,and the classification accuracies for the four zones(from outer to inner zones)on the colonies on RBM were 99.77%,9935%,99.75%and 99.60%and 99.77%,9939%,99.31%and 98.22%for colonies on MAM.In addition,a new score plot of PC2 and PC3 was used to differ-entiate the colonies incubated on RBM and MAM for 6 days.Then characteristic wavelengths of 1067,1195,1279,1369,1459,1694,1834 and 1929 nm were selected from the loading of PC2 and PCg.Based on them,a new SVM model was developed to diferentiate colonies on RBM and MAM with accuracy of 100.00%and 9999%,respectively.In conclusion,SWIR hyperspectral image is a powerful tool for evaluation of growth characteristics of A.parasiticus incubated in diferent culture media.
文摘In behaviour recognition, the development of the DL （Deep Learning） method introduced massive improvements in the field of artificial intelligence, where DL represents an upgrade of the present ANN （artificial neural network） architecture. Deep Learning as a comprehensive new field of artificial intelligence completely covers the neural networks architecture that is devised to carry out certain forms of identification, such as behaviour, forms of things, trends, similarities in complex forms, etc. Regarding thermography in energy, the cases used to illustrate this are photographs of active energy components in the plant. Failures that are seen with thermography cannot be recognized by other methods. However, an expert needs to do segmentation of focusing and classification of failures. The need for daily sampling and expert work is growing. With the DL method, it can be done in real time any time. One of the popular network architectures for using DL in image analysis is the recognition algorithm--CNN （convolution neural network）. Traditional artificial intelligence methods require determining factors and computations, leading to training algorithm. Machine learning has important features as welt as the right weight to make decisions about new input data. This work presents DL as a flexible and adaptive method for the analysis of thermal images of energy facilities, as well as a tool used for the construction and implementation of an efficient fault analysis on the 10/0.4 kV service transformer.
基金supported by the National Natural Science Foundation of China(Nos.52225402 and U1910206).
文摘Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.
