Work

DWCF-YOLO: tunnel leakage detection and segmentation based on multi-level wavelet transform convolution

Year: 2026

Type: article

Abstract: Subway tunnels are essential components of urban transportation systems, and water leakage can threaten their structural safety. Accurate detection and segmentation of leakage regions are therefore critical. This study develops a tunnel water leakage dataset from point-cloud data collected using a mobile measurement platform and converts the point clouds into grayscale images. We propose DWCF-YOLO, an enhanced YOLOv11-based network for water leakage detection and segmentation. The model incorporates multi-level wavelet transform convolution to extract multi-frequency features and promote local–global feature fusion, improving the identification of leakage areas at different scales. In addition, content-guided attention provides channel- and spatial-directional guidance for multi-scale feature maps, enhancing sensitivity to leakage regions. Experimental results show that DWCF-YOLO achieves 96.8% accuracy, 97.8% recall, 98.4% F1-score, 98.9% precision, 96.8% mIoU, and 97.8% mPA. Compared with mainstream models, it obtains the best performance in four of the six evaluation metrics, demonstrating its effectiveness and reliability for tunnel water leakage detection.

Source: Urban Lifeline

DOI: 10.1007/s44285-026-00077-4

Authors: Kaikun Zhang, Shibiao Xu, Qiuzhao Zhang, Wei Duan, Shubi Zhang

Institutions: China University of Mining and Technology, Nanjing Surveying and Mapping Research Institute (China)

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Clean water and sanitation

Open Access status: diamond

Funder: National Natural Science Foundation of China