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Automated Detection and Segmentation of Cracks in Urban Underground Structures Based on YOLOv8-SAM2
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DOI：10.3390/buildings16163211.png)
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Abstract
En 中文
With the expansion of urban underground space, the structural safety of underground infrastructure has become increasingly critical. The urban underground utility tunnel is a typical deeply buried lifeline project. Its internal environment is humid and confined, and the structure is subjected to long-term heavy loads and earth pressure. Under such conditions, micro-cracks readily propagate into leakage channels and eventually cause structural damage. However, the slender morphology, low contrast, and complex background of cracks make it difficult for traditional inspection methods and general-purpose models to achieve reliable identification and accurate segmentation. This study proposes a two-stage framework that combines YOLOv8 with SAM2 and incorporates a coordinate attention module for high-quality crack segmentation. For image processing, a topology-aware post-processing strategy is introduced, together with a scoring function based on crack morphological features and a post-processing constraint mechanism, to ensure crack continuity and geometric consistency and to mitigate the over-segmentation that may occur during segmentation. YOLOv8-SAM2 achieves 85.2%, 90.5%, 77.2%, and 71.6% in mIoU, mDice, Recall, and Precision, respectively. Compared with YOLOv8-seg, mIoU and Precision are improved by 46.3 and 17.2 percentage points, respectively; compared with the baseline SAM2, mIoU and Precision are improved by 23.0 and 16.9 percentage points, respectively. In summary, on the self-built underground utility tunnel crack dataset, the proposed model significantly outperforms standalone YOLOv8 segmentation and the direct application of SAM2 in terms of intersection-over-union and precision, demonstrating its high-quality segmentation capability.
Keywords:
crack segmentation
two-stage segmentation model
deep learning
SAM2
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3.1
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1.7W
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