Automatic bridge crack detection using boundary refinement based on real‐time segmentation network
Gang Li, Tong Liu, Zhongyuan Fang, Qian Shen, Jawad Ali
Abstract
The appearance of surface cracks is an important feature of the early safety of the architecture being threatened, which is of great significance to maintaining the safety of the bridge. Bridge safety maintenance is still a challenging issue owing to noise interference and unclear bridge images. The detection method based on deep learning has achieved remarkable result in semantic segmentation, target detection, and other fields, but still be unable to focus on balancing speed and accuracy. Wherefore, we designed a new crack detection network-dense boundary refinement network (DBR-Net), which combines the advantages of STDC-Net (Short-Term Dense Concatenate network) and refinement network. STDC-Net mainly improves the detection rate by eliminating redundant structures and optimizes the detailed information by binary cross-entropy loss and dice loss. Finally, the detail aggregation module combines the shallow spatial details with the deep semantic information to predict the segmentation results. Among them, the refinement network refines the segmentation results of the offset map generated by the training stage and the test stage. We verified the feasibility of DBR-Net on three datasets. At the same time, the detection accuracy rate on our self-made crack dataset reached 97.54%. What's more worth mentioning is that our detection rate has reached 37.0 images per second (IPS), realizing real-time crack detection.