WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel

Water leakage recognition plays a significant role in ensuring the safety of shield tunnel lining. However, current models cannot meet the engineering requirements because the tunnel environment is complex. In this concern, a one-stage deep learning model is developed for water leakage recognition. First, we design an attention module to reduce background noise interference. Second, an edge refinement algorithm is proposed to refine the mask of water leakage region. Furthermore, a mixed data augmentation is developed to enhance the robustness of model. Experimental results indicate an average precision (AP) is up to 60%, and a recognition speed is 26 frames per second (FPS). This determines that our proposed network is lightweight and has advantages over peer methods.