Asphalt Pavement Crack Detection Method Based on Improved YOLOv5
A improved YOLOv5 asphalt pavement crack detection method is proposed to address the i-ssues of complex crack image backgrounds,small detection targets,poor detection performance,and missed detections in YOLOv5 crack detection.Firstly,the lightweight Mobilenet v3 network,as the fea-ture extraction network of YOLOv5,is used to reduce the complexity of the model and speed up reaso-ning.Secondly,an efficient channel attention mechanism(CBAM)is employed to enhance the net-work's ability to capture and fuse local features.Finally,an embedded Panet module is used to en-hance the multi-scale feature expression ability of crack images and improve the detection performance of small targets.The experimental results show that compared to the original YOLOv5 algorithm,the im-proved YOLOv5 algorithm improves the mAP of asphalt pavement crack detection by 5.5%,reduces the number of model parameters by 86.3%,and reduces image detection time by 75.8%.
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