Aiming at the problems of low detection accuracy,error detection and missing detection in road surface crack target detection by deep learning,a road surface crack detection method based on improved YOLOv5 is proposed.Firstly,the S2-MLPv2 attention module was added to the backbone network of YOLOv5 to improve the model's accurate positioning ability.Secondly,in the neck part,GSConv+Slim-neck combined structure is used to reduce the complexity of the model and improve the accuracy.Fi-nally,the activation function is changed to FReLU,and ReLU and PReLU are extended to 2D activation function,which solves the space insensitivity problem in activation function.The experimental results indicate that compared to the traditional YOLOv5,this model achieves a 3-percentage-point increase in mAP50,demonstrating better detection performance and meeting the requirements for accurate detection.
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