Tunnel leakage detection based on infrared imaging and machine learning in complex illumination

Abstract Accurate and effective water leakage detection in tunnel linings under complex illumination conditions is crucial for ensuring operational safety. This paper proposed a method for detecting water leakage based on infrared vision and an enhanced U-shaped network (UNet) architecture. Initially, the lightweight Azure Kinect sensor was employed to capture both infrared and RGB images of tunnel linings under different illumination conditions. Three datasets: the RGB image under normal illumination (NRGB), the infrared image under normal illumination (NIR), and the infrared image under low illumination (LIR) were created. Subsequently, an enhanced UNet architecture (UKAN) was introduced for water leakage segmentation in tunnel linings. Moreover, Score-weighted and Layer-wise class activation mapping (CAM) were adopted to analyze the model’s decision-making process. Experimental results revealed that the average detection accuracy for the NIR exceeded that of the NRGB by 2.4%, while the LIR was higher by 1.43%. The proposed UKAN architecture achieved optimal mean intersection over union scores of 84.1%, 86.5%, and 85.7% on NRGB, NIR, and LIR. Additionally, visual interpretability analysis showed that the UKAN’s learning process was smooth, gradually shifting its focus from low-level features to target regions. This methodology facilitates efficient and accurate water leakage detection in tunnel linings under varying illumination conditions.