Leakage-YOLO：隧道场景下裂缝漏水的实时目标检测算法

doi:10.3778/j.issn.1002-8331.2408-0319

摘要/Abstract

摘要： 隧道盾构裂缝漏水问题的检测对于保障隧道结构的安全性和延长其使用寿命至关重要。随着目标检测技术的发展，先进的检测技术逐渐被应用于隧道盾构裂缝漏水区域的自动检测，以提高检测效率和精度。因此，为进一步提高裂缝漏水区域的检测精度并实现实时的隧道盾构裂缝漏水检测，在YOLOv8的基础上提出了目标检测算法Leakage-YOLO。该算法通过在检测颈中引入区域焦点注意力模块（regional spotlight attention），更好地融合全局与局部特征信息，增强对关键区域特征的提取能力，进而有效解决了裂缝漏水区域显著特征难以提取的问题。此外，通过改进检测头，提出一种新的SE-Head结构，进一步增强了对细节边缘特征的捕捉能力，有效改善了裂缝漏水区域定位不精确的问题。在真实场景的公开数据集的实验结果表明，改进后的算法相比原算法在AP、AP0.5、AP0.75上分别提高了4.7、4.9、6.7个百分点，并与其他主流算法对比，验证了所提的Leakage-YOLO的有效性和优越性。

关键词: 隧道盾构裂缝漏水检测, Leakage-YOLO, 注意力机制, 关键区域特征

Abstract: The detection of cracks and water leakage in tunnel shield linings is essential for ensuring the structural safety and extending the service life of tunnels. With the advancement of object detection technologies, advanced techniques have been increasingly applied to the automatic detection of cracks and leakage areas in tunnel shield linings to improve detection efficiency and precision. Therefore, to further improve the precision of detecting these areas and to achieve real-time detection, the Leakage-YOLO algorithm is proposed, based on YOLOv8. The algorithm introduces a regional spotlight attention (RSA) module into the detection neck, which better integrates global and local feature information, thereby enhancing the ability to extract key regional features. This effectively addresses the challenge of extracting significant features in crack and leakage areas. Additionally, by modifying the detection head, a novel SE-Head structure is proposed, further enhancing the ability to extract detailed edge features, effectively improving the precision of crack and leakage area localization. Experimental results on public datasets in real-world scenarios demonstrate that the improved algorithm outperforms the original algorithm with increases of 4.7, 4.9, and 6.7 percentage points in AP, AP0.5, and AP0.75, respectively. Compared with other mainstream algorithms, the effectiveness and superiority of the Leakage-YOLO are further verified.

Key words: tunnel shield crack and leakage detection, Leakage-YOLO, attention mechanism, key region feature

陈灿森, 刘巍. Leakage-YOLO：隧道场景下裂缝漏水的实时目标检测算法[J]. 计算机工程与应用, 2025, 61(6): 118-127.

CHEN Cansen, LIU Wei. Leakage-YOLO: Real-Time Object Detection Algorithm for Crack and Leakage in Tunnel Scenarios[J]. Computer Engineering and Applications, 2025, 61(6): 118-127.

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