Research on Detection and Identification of Pavement Crack Diseases Based on Improved Yolov5s
Highway pavement cracks are an important influencing factor in asphalt pavement diseases,and pavement crack detection is an im-portant part of pavement maintenance.A pavement crack disease detection model based on improved Yolov5s is proposed to address the prob-lems of missed detections,false detections,and low recognition accuracy in detection algorithms for highway pavement cracks.Firstly,the at-tention mechanism module CBAM is adopted to learn target features and positional features,and to increase useful feature weights;Secondly,the Decoupled decoupling head method is proposed to separate the feature maps through different branches for processing,in order to improve training accuracy;Finally,an improved α DIoU loss function is proposed to replace the CIoU loss function in the original model,and α=3 is selected to enhance the loss gradient value of the high IoU object and the regression effect of the box.The experiment shows that the improved model has an average detection accuracy of 92.8%,a recall rate of 94.5%,and an mAP value of 96.5%,which is 1.8%higher than the origi-nal model.This proves that the improved model has a high improvement effect on detection accuracy and can meet the recognition and detec-tion tasks of highway pavement cracks.
road crack detectionYolov5sattention mechanismdecoupling headloss function
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