A tunnel water leakage detection method based on multi-scale edge information fusion

doi:10.11947/j.AGCS.2026.20260007

Abstract

Abstract:

With the rapid development of urban rail transit, the demand for water leakage detection of subway tunnels is increasing. There are many attachments on the surface of tunnel lining, which block the water leakage area, and some of the water leakage boundaries are blurred, which brings great challenges to the detection. The existing methods have insufficient ability to capture the edge features of water leakage in complex tunnel background, which limits the detection accuracy and robustness of water leakage areas with blurred boundaries or variable shapes. In view of the above problems, this paper proposes a tunnel water leakage detection method based on multi-scale edge information fusion. The method consists of multi-feature fusion image enhancement algorithm and global-local edge information fusion YOLO (GLEF-YOLO) detection model. In the image preprocessing stage, the wavelet transform enhanced anisotropic diffusion filtering and adaptive histogram equalization are combined to enhance the edge details and local contrast of the water leakage area. In the detection model, a multi-scale edge information transfer module (MEIT) and a global-local perception fusion module (GLAFusion) are introduced to transmit shallow edge information across layers, and local details and global semantic features are fused to improve the recognition ability of the model for irregular water leakage targets in complex backgrounds. The experimental results show that the mAP of GLEF-YOLO is 8.4 percentage points higher than that of the baseline model. Compared with the recent excellent target detection methods, it has achieved the best results in all indicators, and has higher accuracy in the detection of water leakage in complex backgrounds, and is more robust to pipeline, appendage occlusion and background texture interference. The effectiveness of the proposed method is verified, which provides an effective solution for the intelligent and high-precision detection of tunnel water leakage.

Key words: tunnel water leakage, image enhancement, target detection, 3D laser scanning, multi-scale edge information transfer

CLC Number:

P237

Shuo LIU, Haili SUN, Ruofei ZHONG. A tunnel water leakage detection method based on multi-scale edge information fusion[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(6): 1116-1127.

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输入数据	mAP	精确率	召回率
YOLOv8（原始图像）	71.9	70.2	68.2
YOLOv8（增强图片）	73.6	71.9	71.2

方法	mAP	精确率	召回率
YOLOv11	77.9	72.3	75.8
YOLOv12	77.9	74.8	75.4
RT-DETR	77.5	72.0	72.3
FBRT-YOLO	74.5	72.5	70.6
Hyper-YOLO	78.3	73.6	74.3
GLEF-YOLO	82.0	77.0	79.5

模型结构	mAP	精确率	召回率
YOLOv8	73.6	71.9	71.2
YOLOv8+MEIT	80.0	76.8	75.3
YOLOv8+MEIT+GLAFusion	82.0	77.0	79.5