Abstract: The existing target detection models are difficult to accurately detect diseases with severe size change, blurred boundaries, and noise interference in complex backgrounds due to the weak adaptability in tunnel lining leakage detection. To address this challenge, a YOLOv11-CMDS model is proposed. For insufficient processing of multiscale feature inputs by the C3K2 module in the backbone network, a C3K2-MSCB module is created by introducing a multiscale MSCB module and integrating it with the original C3K2 module to enhance the feature extraction ability of the model’s backbone network for diseases with severe size changes. In addition, to address the distortion of image edge details caused by the original upsampling in image feature processing, DySample is introduced to adaptively concentrate the sampling points on the key details of water leakage diseases, and enhance the model’s ability to restore the details of blurred disease boundaries. Finally, the SENetV2 attention mechanism is embedded to further enhance the model’s ability to perceive diseases through global information compression and channel weight redistribution. Experiments are conducted on a self-collected tunnel lining leakage dataset as the test sample. The results show that: (1) In the ablation experiment, the mAP50%, Precision, Recall, and F₁ scores of the YOLOv11-CMDS model are 84.8%, 81.7%, 74.7%, and 78.0%, respectively, which are 3.1%, 3.8%, 2.2%, and 3.0% higher than those of the baseline model YOLOv11n, respectively. (2) Through synergistic effect of multiscale feature enhancement, dynamic detail restoration, and the attention mechanism, the model effectively improves the detection accuracy of tunnel lining water leakage diseases.

Key words: tunnel engineering, target detection, water leakage, deep learning, multi-scale