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Deep learning-based YOLO for crack segmentation and measurement in metro tunnels
Automation in Construction ( IF 9.6 ) Pub Date : 2024-10-09 , DOI: 10.1016/j.autcon.2024.105818 Kun Yang, Yan Bao, Jiulin Li, Tingli Fan, Chao Tang
Automation in Construction ( IF 9.6 ) Pub Date : 2024-10-09 , DOI: 10.1016/j.autcon.2024.105818 Kun Yang, Yan Bao, Jiulin Li, Tingli Fan, Chao Tang
To address the increasing issue of cracks in metro shield tunnels, this paper proposes the YOLOv8-GSD model, which integrates DySnakeConv, BiLevelRoutingAttention, and the Gather-and-Distribute Mechanism with the YOLOv8 algorithm. This model is designed for detecting and segmenting cracks in tunnel linings and employs a pixel grouping method to measure crack length and width. Using a real crack dataset from a subway section in Suzhou, China, comparative experiments with YOLOv8x, BlendMask, SOLOv2, and YOLACT demonstrate that YOLOv8-GSD excels in segmentation performance (AP of 82.4 %) and accuracy (IoU of 0.812). The measured crack dimensions show an error within 5 % compared to actual values, confirming the model's effectiveness. These results highlight the potential of YOLOv8-GSD for enhancing the maintenance and safety of metro tunnels.
更新日期：2024-10-09
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