A Transformer-based Cascade Method for Segmenting Bridge Cracks from High-resolution Images
To fully leverage the advantages of the Transformer model in high-resolution(HR)bridge crack image segmentation,a refined cascaded segmentation method,Cascade CATransUNet,based on the Transformer and Coordinate Attention(CA)mechanism was proposed.Firstly,a TransUNet-based crack feature extraction module was introduced to preliminarily extract coarse-grained crack features at three scales from low-resolution(LR)crack images.The CA mechanism was incorporated into the skip-connection structure of TransUNet to enhance the representation of subtle crack features.Then,based on the extracted coarse-grained crack features at the three scales,two refined operation modules based on physical cascaded structures were designed to sequentially restore fine-grained pixel features of the crack body and edge region from both global and local dimensions.Additionally,to fully utilize the advantages of multi-scale features in the fine-grained representation of crack boundaries,a multi-scale cascaded loss with an active boundary regression term is introduced during the training process.Ablation experiments conducted on HR bridge crack images captured by the unmanned aerial vehicle(UAV)demonstrated the effectiveness of each proposed component.Finally,the comparative experiment conducted on 4 K-resolution bridge crack images revealed that the Cascade CATransUNet surpasses the state-of-the-art high-resolution(HR)refinement networks Segfix and CascadePSP,both of which rely on traditional convolutional neural networks(CNNs).Notably,the Cascade CATransUNet achieved significant enhancements of 5.04％and 7.10％in mean Intersection over Union(mIoU)and mean Boundary Accuracy(mBA),respectively,while retaining identical GPU memory requirements.By adopting the Cascade CATransUNet,it becomes feasible to perform fine-grained segmentation of HR crack images,enabling structural inspectors to obtain more comprehensive and accurate crack information.Consequently,this provides valuable technical support for bridge safety assessment and maintenance decision-making processes.
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