Multi-scale triple-attention network for pixelwise crack segmentation

Abstract

Currently, intelligent crack detection is of great value for the maintenance of infrastructure, of which the most significant kind in China is roads. For pavement defects, the pavement can be repaired and maintained in a timely manner with an accurate defect detection task, which significantly reduces the occurrence of hazards. However, the detection of pavement defects remains a great challenge owing to many difficulties, for example, complex backgrounds, microdefects, various defect shapes and sizes, class imbalance issues, etc. Recently, deep learning has demonstrated its superior performance on pixelwise image segmentation, but some issues still exist on demanding pixelwise image segmentation, for instance, limited receptive field, insufficiency processing of local features, information loss issue generated by pooling operations, etc. Based on all of the above issues, a multiscale triple-attention network, named MST-Net, is proposed for end-to-end pixelwise crack detection. First, a multiscale input strategy is applied to the proposed segmentation network to capture more context information. Meanwhile, it can capably reduce the effect of the information loss issue generated by pooling operations. Second, to realize effective feature representation of local features, an additive attention fusion (AAF) block is proposed to guide feature learning to capture both global and local contexts. In addition, faced with the crack detection task with class imbalance issues, a triple attention (TA) block is proposed to detect spatial, channel and pixel attention information to suppress the background and useless information, which is conducive to the characterization of microcracks. Finally, aiming at the limited receptive field, a multiscale feature aggregation unit is proposed for feature fusion to increase the detection ability of multiscale defects. To better guide network training, a deep supervision mechanism is also introduced to speed up the convergence of the proposed segmentation model and improve the performance of defect segmentation. The related evaluation and detection experiments are carried out on three public datasets on crack segmentation, and the comparison experiments with the mainstream segmentation models show that the proposed segmentation network achieves excellent performance on pixelwise crack detection.