Abstract: Aiming at the current problems of weak detection ability and low segmentation accuracy for small cracks,an improved U-Net model is proposed to detect road cracks and improve detection ability and segmentation accuracy.This paper designs a new module,multi scale depth separated convolutional block(MSDWBlock),which is applied in the encoder and decoder sections.Through its depthwise separable convolution,the model's ability is enhanced,the model's receptive field is expanded,and a C2G attention mechanism module is introduced in the skip connection section to enhance the model's perception of crack features.And atrous spatial pyramid pooling(ASPP) and discrete wavelet transformation(DWT) are introduced.ASPP helps to capture the characteristics of cracks by operating at multiple scales,while DWT can reduce the loss of crack spatial information during convolutional pooling and preserve crack edge information.This structural design makes the network more focused on the characteristics of cracks,thereby improving the accuracy of crack detection.It has been demonstrated through experiments that the accuracy of the proposed model is better than that of advanced models such as U-Net,Segnet,and U2net.On the CFD dataset,mIoU and F1 reaches 78.51% and 0.868 respectively.These results indicate that the proposed method can effectively improve the perfor-mance of road crack detection.

Key words: Crack detection, U-net neural network, Depthwise separable convolutional, Attention mechanism, Spatial pyramid, Wavelet transform