隧道渗漏水病害是影响盾构隧道结构安全和运营稳定性的主要因素。传统人工巡检效率低，受主观判断影响大，且可见光图像难以识别浅层及干扰复杂环境下的渗漏水。针对隧道渗漏水高效、标准化识别的应用需求，搭建一套由12台电动调焦红外热像仪组成的移动采集系统，实现对隧道衬砌的高分辨率、多视角红外图像采集。针对现有标注方式效率低、标准不统一的问题，提出一种基于HSV色彩空间的自动标注方法，通过色相-饱和度-亮度阈值在统一成像条件下实现渗漏水区域的快速掩膜生成。与传统人工标注和SAM辅助标注方法相比，HSV标注在隧道红外图像场景中具有更高的一致性和更低的人工修正成本。在算法模型方面，基于Unet结构，引入Mobilenet深度可分离卷积、全尺度padding卷积以及跨层连接优化策略，构建改进轻量化Unet模型。深度可分离卷积可有效减少87.3%参数量，填充卷积保持特征图尺寸一致性，跨层连接裁剪在保持高精度的同时提高推理速度。基于199张真实隧道红外图像及增强后的1 000张数据集开展试验，改进Unet模型在测试集上的准确率达99.76%，IoU（交并比）达98.23%，推理速度达22.4 FPS； 相较TR-Unet，帧率提升23.1%，参数量减少24.1%。集成开发移动巡检平台样机，实现红外图像多机同步采集与渗漏水在线检测。样机测试结果表明，系统平均帧率达17.8 FPS，可满足隧道巡检车移动检测需求。

Tunnel seepage poses a significant threat to the durability and operational safety of shield tunnels, while manual inspection and visible-light imaging struggle to provide reliable detection. Therefore, a mobile infrared acquisition system consisting of twelve electronically focusing thermal cameras is developed for synchronized, high-resolution imaging of tunnel linings. To reduce labeling cost and address the inconsistency of existing labeling methods, an efficient HSV-based automatic annotation method is proposed, which leverages the stable color-temperature characteristics of infrared images to generate consistent seepage masks with higher efficiency. Compared with manual and SAM-assisted labeling methods, the HSV annotation method achieves higher consistency with lower manual correction cost for tunnel infrared images. A lightweight Unet model is further constructed by integrating MobileNet-based depthwise separable convolutions, full-scale padding-preserved convolutions, and optimized skip-connection pruning strategies, which substantially reduces the number of parameters by 87.3% while maintaining pixel-level accuracy. Experiments conducted on 199 real tunnel infrared images and an augmented dataset of 1 000 samples show that the proposed model achieves 99.76% accuracy, 98.23% IoU, and 22.4 FPS, outperforming TR-Unet in both accuracy and inference speed. A prototype inspection system combining the proposed model and the multi-camera platform achieves 17.8 FPS in real tunnel tests, demonstrating the practicality and effectiveness of the proposed method for intelligent tunnel seepage detection.