SwinCrack: Pavement crack detection using convolutional swin-transformer network

Abstract

By leveraging deep learning methods, pavement crack detection can be more automatic, efficient, and accurate than manual inspection. To solve the problem of limited receptive field in pure CNN-based crack detection networks, we proposed an end-to-end detection network based on Swin-Transformer, called SwinCrack. SwinCrack can produce more accurate and continuous descriptions of pavement cracks by modeling long-range interactions and adaptive spatial aggregation compared to CNN-based detection models. Furthermore, to delineate crisp and accurate crack boundaries, we introduced convolution operations to Swin-Transformer for more local and detailed crack information. Convolutional Patch Embedding Layer (CPEL), Convolutional Swin-Transformer Block (CSTB), and Depth-convolution Forward Network (DFN) are proposed and embedded into SwinCrack to capture more spatial contexts. Also, Convolutional Attention Gated Skip Connection (CAGSC) is designed to suppress background interference in low-level features. Furthermore, five evaluation experiments on SwinCrack and an ablation study on the four proposed modules are performed. The attention maps of the SwinCrack are visualized to give a better insight into the contribution of each convolutional module embedded. Evaluation results show that SwinCrack gains OIS values of 0.781 to 0.849 and a maximum 4.4% improvement on OIS among the six public crack datasets.