Water leakage inspection in the tunnels is a critical engineering job that has attracted increasing concerns.Leakage area detection via manual inspection techniques is time-consuming and might produce unreliablefinding...Water leakage inspection in the tunnels is a critical engineering job that has attracted increasing concerns.Leakage area detection via manual inspection techniques is time-consuming and might produce unreliablefindings, so that automated techniques should be created to increase reliability and efficiency. Pre-trainedfoundational segmentation models for large datasets have attracted great interests recently. This paper proposes a novel SAM-based network for accurate automated water leakage inspection. The contributions of thispaper include the efficient adaptation of the SAM (Segment Anything Model) for shield tunnel water leakagesegmentation and the demonstration of the application effect by data experiments. Tunnel SAM Adapter hassatisfactory performance, achieving 76.2 % mIoU and 77.5 % Dice. Experimental results demonstrate that ourapproach has advantages over peer studies and guarantees the integrity and safety of these vital assets whilestreamlining tunnel maintenance.展开更多
基金funded by the National Natural Science Foundation of China(Nos.62171114,52222810)the Fundamental Research Funds for the Central Universities(No.DUT22RC(3)099).
文摘Water leakage inspection in the tunnels is a critical engineering job that has attracted increasing concerns.Leakage area detection via manual inspection techniques is time-consuming and might produce unreliablefindings, so that automated techniques should be created to increase reliability and efficiency. Pre-trainedfoundational segmentation models for large datasets have attracted great interests recently. This paper proposes a novel SAM-based network for accurate automated water leakage inspection. The contributions of thispaper include the efficient adaptation of the SAM (Segment Anything Model) for shield tunnel water leakagesegmentation and the demonstration of the application effect by data experiments. Tunnel SAM Adapter hassatisfactory performance, achieving 76.2 % mIoU and 77.5 % Dice. Experimental results demonstrate that ourapproach has advantages over peer studies and guarantees the integrity and safety of these vital assets whilestreamlining tunnel maintenance.
