Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurre...Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.展开更多
In this study, expanded graphite and natural graphite were introduced into resin-based friction materials, and the tribological behavior of the composites was investigated. The tribo-performance of the two friction co...In this study, expanded graphite and natural graphite were introduced into resin-based friction materials, and the tribological behavior of the composites was investigated. The tribo-performance of the two friction composites was evaluated using a constant speed friction tester. The results showed that the expanded graphite composite (EGC) displayed better lubricity in both the fading and the recovery processes. The wear rate of the EGC decreased by 22.43%more than that of the natural graphite composite (NGC). In the fading process, and the EGC enhanced the stability of the coefficient of friction. The recovery maintenance rate of the NGC was 4.66% higher than that of the EGC. It can be concluded that expanded graphite plays an important role in the formation of a stable contact plateau and can effectively reduce the wear.展开更多
基金supported by the National Natural Science Foundation of China(62072158,U2004163)the Key Research and Development Special Projects of Henan Province(231111221500)Science and Technology Project of Henan Province(232102210158,242102210197).
文摘Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies.Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents,thereby providing a guarantee for the safety of drivers.However,detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds,varying target scales,and different resolutions.Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios,this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5.The algorithm integrates Attention-based Intra-scale Feature Interaction(AIFI)into the backbone network,enabling it to focus on enhancing feature interactions within the same scale through the attention mechanism.By emphasizing important features,this approach improves detection accuracy,thereby enhancing performance in complex backgrounds.Additionally,a Triple Feature Encoding(TFE)module has been added to the neck network.This module utilizes multi-scale features,encoding and fusing them to create a more detailed and comprehensive feature representation,enhancing object detection and localization,and enabling the algorithm to fully understand the image.Finally,the shape-IoU(Intersection over Union)loss function is adopted to replace the original IoU for more precise bounding box regression.Comparative evaluation of the improved YOLOv5 distraction detection algorithm against the original YOLOv5 algorithm shows an average accuracy improvement of 1.8%,indicating significant advantages in solving distracted driving problems.
基金This work was financially supported by the National Key Research Program of China(2016YFA0201001)Major scientific and technological innovation in Hubei(2017AAA112 and 2018AAA015)+1 种基金the Open research project of the Ministry of Education's Engineering Research Center of Nano-Geo Materials(NGM2017KFO11)the laboratory open foundation of the 2016-2017 academic year(SKJ2018052).
文摘In this study, expanded graphite and natural graphite were introduced into resin-based friction materials, and the tribological behavior of the composites was investigated. The tribo-performance of the two friction composites was evaluated using a constant speed friction tester. The results showed that the expanded graphite composite (EGC) displayed better lubricity in both the fading and the recovery processes. The wear rate of the EGC decreased by 22.43%more than that of the natural graphite composite (NGC). In the fading process, and the EGC enhanced the stability of the coefficient of friction. The recovery maintenance rate of the NGC was 4.66% higher than that of the EGC. It can be concluded that expanded graphite plays an important role in the formation of a stable contact plateau and can effectively reduce the wear.
