Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability...Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability to fully extract fused image information.Therefore,a multimodal medical image fusion method based on mask optimization and parallel attention mechanism was proposed to address the aforementioned issues.Firstly,it converted the entire image into a binary mask,and constructed a contour feature map to maximize the contour feature information of the image and a triple path network for image texture detail feature extraction and optimization.Secondly,a contrast enhancement module and a detail preservation module were proposed to enhance the overall brightness and texture details of the image.Afterwards,a parallel attention mechanism was constructed using channel features and spatial feature changes to fuse images and enhance the salient information of the fused images.Finally,a decoupling network composed of residual networks was set up to optimize the information between the fused image and the source image so as to reduce information loss in the fused image.Compared with nine high-level methods proposed in recent years,the seven objective evaluation indicators of our method have improved by 6%−31%,indicating that this method can obtain fusion results with clearer texture details,higher contrast,and smaller pixel differences between the fused image and the source image.It is superior to other comparison algorithms in both subjective and objective indicators.展开更多
In the context of global COVID-19 epidemic preparedness,the extensive use of disposable surgical masks(DSM)may lead to its emergence as a main new source of microplastics in the environment.Nowadays,DSMs have become a...In the context of global COVID-19 epidemic preparedness,the extensive use of disposable surgical masks(DSM)may lead to its emergence as a main new source of microplastics in the environment.Nowadays,DSMs have become a non-negligible source of plastic waste in aquatic environment,however,less research has been done on DSM after biofilm colonization in freshwater environment.The study investigated the microbial community of DSM-associated biofilms by 16S rRNA gene sequencing.Analysis of the microbial community in the middle and inner/outer layers of the DSM showed that the middle layer was different from the remaining two layers and that potential pathogens were enriched only in the middle layer of the DSM.Herein,we focused on the middle layer and explored the characterization properties and extracellular polymeric substances(EPS)components changes during biofilm formation.The results showed that the EPS components varied with the biofilm incubation time.As the formation of biofilm,the protein(PN)and polysaccharide(PS)in EPS showed an overall increasing trend,and the growth of PS was well synchronized with PN.Three fluorescent components of EPS were determined by the three-dimensional excitation emission matrix(3D-EEM),including humic acid-like,fulvic acid-like,and aromatic protein-like components.The percentage of fluorescent components varied with increasing biofilm development time and then stabilized.Fourier transform infrared spectroscopy(FTIR)characterization results elucidated the emergence of oxygen-containing functional groups during biofilm formation.Moreover,the hydrophilicity increased with biofilm development.In conclusion,the environmental behavior and ecological risks of DSM in aquatic environment deserve urgent attention in future studies.展开更多
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability to fully extract fused image information.Therefore,a multimodal medical image fusion method based on mask optimization and parallel attention mechanism was proposed to address the aforementioned issues.Firstly,it converted the entire image into a binary mask,and constructed a contour feature map to maximize the contour feature information of the image and a triple path network for image texture detail feature extraction and optimization.Secondly,a contrast enhancement module and a detail preservation module were proposed to enhance the overall brightness and texture details of the image.Afterwards,a parallel attention mechanism was constructed using channel features and spatial feature changes to fuse images and enhance the salient information of the fused images.Finally,a decoupling network composed of residual networks was set up to optimize the information between the fused image and the source image so as to reduce information loss in the fused image.Compared with nine high-level methods proposed in recent years,the seven objective evaluation indicators of our method have improved by 6%−31%,indicating that this method can obtain fusion results with clearer texture details,higher contrast,and smaller pixel differences between the fused image and the source image.It is superior to other comparison algorithms in both subjective and objective indicators.
基金Supported by the Natural Science Foundation of Shandong Province(Nos.ZR2022MD115,ZR202111160067)。
文摘In the context of global COVID-19 epidemic preparedness,the extensive use of disposable surgical masks(DSM)may lead to its emergence as a main new source of microplastics in the environment.Nowadays,DSMs have become a non-negligible source of plastic waste in aquatic environment,however,less research has been done on DSM after biofilm colonization in freshwater environment.The study investigated the microbial community of DSM-associated biofilms by 16S rRNA gene sequencing.Analysis of the microbial community in the middle and inner/outer layers of the DSM showed that the middle layer was different from the remaining two layers and that potential pathogens were enriched only in the middle layer of the DSM.Herein,we focused on the middle layer and explored the characterization properties and extracellular polymeric substances(EPS)components changes during biofilm formation.The results showed that the EPS components varied with the biofilm incubation time.As the formation of biofilm,the protein(PN)and polysaccharide(PS)in EPS showed an overall increasing trend,and the growth of PS was well synchronized with PN.Three fluorescent components of EPS were determined by the three-dimensional excitation emission matrix(3D-EEM),including humic acid-like,fulvic acid-like,and aromatic protein-like components.The percentage of fluorescent components varied with increasing biofilm development time and then stabilized.Fourier transform infrared spectroscopy(FTIR)characterization results elucidated the emergence of oxygen-containing functional groups during biofilm formation.Moreover,the hydrophilicity increased with biofilm development.In conclusion,the environmental behavior and ecological risks of DSM in aquatic environment deserve urgent attention in future studies.
