External Attention Based TransUNet and Label Expansion Strategy for Crack Detection

Crack detection is an indispensable premise of road maintenance, which can provide early warning information for many road damages and save repair costs to a large extent. Because of the security and convenience, many image processing technique (IPT) based crack detection methods have been proposed, but their performances often cannot meet the requirements of practical applications because of the complex texture structure and seriously imbalanced categories. To address the aforementioned problem, we present an external attention based TransUNet for crack detection. Specifically, we tackle the TransUNet as the backbone of our detection framework, which can propagate the detailed texture information from shallow layers to corresponding deep layers through skip connections. Besides, the Transformer Block equipped in the second last convolution layer of the encoding component can explicitly model the long-range dependency of different regions in an image, which improves the structural representation ability of the framework and hence alleviates the interference from shadow, noise, and other negative factors. In addition, the External Attention Block equipped in the last convolution layer of the encoding component can effectively exploit the dependency of crack regions among different images, and further enhance the robustness of the framework. Finally, combined with the Focal Loss, the proposed label expansion strategy can further alleviate the category imbalance problem through transforming semantic categories of non-crack pixels distributed in the neighbors of corresponding crack pixels.