Abstract: To address the problem of poor edge localization in deep learning-based methods and low efficiency of traditional percolation algorithms for tunnel crack detection, a two-stage approach that integrates an improved DeepLabV3+ with a skeleton-guided percolation algorithm was proposed.In the first stage, an enhanced DeepLabV3+ model—incorporating a CBAM attention module, an optimized ASPP module, and a Dice loss function—was developed to achieve high-recall crack pre-segmentation. In the second stage, a skeleton-guided percolation growth strategy combined with morphological constraints was applied to refine crack edges and measure crack widths.A tunnel crack dataset containing 20 604 pixel-level annotated images was constructed, covering various tunnel lining regions (crown, haunch, sidewall) and surface conditions (dry, wet, stained).Experimental results on this dataset showed that the pre-segmentation module achieved an accuracy of 90.1% and a recall of 86.7%.The improved percolation algorithm increased the precision to 98.5% while maintaining high recall, and improved computational efficiency by approximately 20 times.Engineering validation demonstrated a detection rate exceeding 84% for cracks wider than 0.1?mm, with a mean absolute error of less than 0.3?mm.The proposed method effectively balanced detection accuracy and computational efficiency, providing a feasible solution for automated tunnel lining crack detection.
陈彰昕, 王刚, 李文锋, 李科, 江松, 刘廷方. 基于改进DeepLabV3+和渗流算法的隧道裂缝检测研究[J]. 隧道与地下工程灾害防治, .
CHEN Zhangxin, WANG Gang, LI Wenfeng, LI Ke, JIANG Song, LIU Tingfang. Research on tunnel crack detection based on improved DeepLabV3+ and percolation algorithm. Hazard Control in Tunnelling and Underground Engineering, 0, (): 1-14.
