Improved U-Net Model for Road Crack Detection Based on Residual and Attention Mechanism

Road cracks are an important part of road safety detection，and with the development of deep learning and computer vision，methods for extracting crack information in road images using deep learning methods are maturing.Existing deep learning road crack detection methods cannot extract small cracks and are affected by background factors，resulting in a decrease in detection accuracy.Based on the Convolutional Block Attention Module（CBAM） attention mechanism and the residual network，a deep learning network model for road crack detection incorporating the residual and attention mechanisms is established by improving the U-Net neural network model.The model embeds the channel attention mechanism and spatial attention mechanism in the up-sampling and down-sampling processes of the U-Net network，respectively.The CBAM attention mechanism performs both global average and global maximum mixed pooling on both channel and spatial dimensions，producing more effective global and local detail information.Meanwhile，integrating residual modules in the U-Net network effectively solves the problems of network gradient disappearance，gradient explosion，and network degradation，further improving the detection ability of road cracks.The experimental results show that compared with the U-Net original network，the F1 value of the U-Net network embedded with CBAM attention mechanism in the up-sampling and down-sampling processes to 81.02%，an increase of 13.76 percentage points.Further，compared with the network that only embeds the CBAM attention mechanism，the F1 value of the network that integrates residual modules and embeds the CBAM attention mechanism in the down-sampling processes reaches 85.82%，an increase of 4.8 percentage points.