Pavement Crack Segmentation Method Based on Bilevel Convolution and Multi-feature Fusion
Aiming at the problems of missing edge details and misjudgment of target in road crack segmentation method under complex background,this article proposed a pavement crack segmentation network based on bilevel convolution and multi-feature fusion.Firstly,the U-Net network was used as the infrastructure to design a bilevel convolutional network to improve the coding part,increase the receptive field,and extract rich context information.Secondly,the coordinate attention module was introduced to optimize the decoding part to further enhance the network's learning of crack edge details.Finally,the generated multilevel features were fed back to the feature fusion module,and the deep and shallow features were effectively fused by stacking channels.In addition,the loss function combined binary cross-entropy loss and Dice loss function,which effectively solved the sample imbalance problem caused by a background larger than crack pixels.The experimental results on CRACK500,CFD,and Cracktree200 show that the MIoU achieved by this method is 76.8%,65.3%,and 53%,respectively,which is 3.5%higher than the result by the existing methods,and it can achieve excellent automatic segmentation effect on road cracks.
万方数据
维普
