渗漏水是输水隧洞的典型病害，其长期发展易导致衬砌结构劣化，严重威胁工程安全。依托停水检修阶段采集的高清影像开展病害精准识别与针对性修复，已成为保障输水系统长效运行的关键手段。然而，受渗漏影像形态不规则、尺度多变、边界模糊及洞内光照不均等因素制约，现有分割算法的精度与鲁棒性往往难以满足实际需求。针对上述挑战，本文提出一种改进型DeepLabV3+的轻量化语义分割方法。该模型以MobileNetV3为轻量级主干网络，显著压缩参数量与计算开销；在编码器中嵌入级联式多尺度上下文聚合模块，依托多级空洞卷积的级联结构，强化对多尺度及细长渗漏水特征的表征能力；在解码阶段融合scSE注意力机制，通过对通道和空间维度的并行重标定，实现渗漏区域的自适应特征增强。在自建数据集上的验证结果表明，模型平均交并比（MIoU）、渗漏水交并比（Leak-IoU）与平均精确率（mPrecision）分别达到91.98%、86.48%和96.41%，参数量降至5.68 M，边缘设备实测FPS为12.20 f∙s^-1。与原始DeepLabV3+及多种主流分割模型相比，所提方法在大幅降低模型复杂度的同时保持了优异的分割性能。该模型轻量化架构高度契合现场边缘设备的部署需求，可为输水隧洞渗漏水的智能识别与工程运维提供可靠的技术支撑。

[Objectives] Water leakage is one of the most prevalent and critical pathological defects in water conveyance tunnels. Its long-term evolution inevitably leads to the deterioration of lining structures, fundamentally threatening the overall engineering safety and operational longevity of hydraulic systems. Consequently, leveraging high-definition inspection imagery captured during maintenance periods to achieve precise defect identification and facilitate targeted, specialized repair efforts has emerged as a crucial strategy for ensuring the safe operation of water conveyance networks. However, current automated method development faces significant hurdles because leakage images in tunnel environments are characterized by highly irregular morphology, multi-scale variations, and inherently blurred boundaries between water and concrete. Furthermore, uneven and insufficient illumination inside the tunnels drastically limits the segmentation accuracy of conventional models. To address these challenging imaging conditions, this study proposes a lightweight semantic segmentation method specifically optimized for water conveyance tunnel leakage, based on an improved DeepLabV3+ architecture. [Methods] The proposed model architecture incorporates three specific modifications to achieve a balance between accuracy and computational efficiency. First, to mitigate high computational costs, the model adopts MobileNetV3 as its lightweight backbone network. This choice replaces traditional, parameter-heavy large-scale feature extraction networks, thereby effectively compressing the number of model parameters and reducing computational overhead to facilitate future deployment on resource-constrained devices. Second, to tackle the issues of varying scales and boundaries, a novel cascaded multi-scale context aggregation module is embedded directly into the encoder stage. By utilizing a specific cascaded structure of multi-level atrous convolutions, this module significantly enhances the network’s multi-scale contextual representation capabilities, specifically improving the extraction and description of features pertaining to multi-scale and slender leakage regions. Third, during the decoding phase, the model integrates the Concurrent Spatial and Channel Squeeze & Excitation (scSE) attention mechanism. The scSE module performs parallel recalibration on the fused features along both the channel and spatial dimensions. This dual-axis recalibration achieves adaptive feature enhancement for target leakage regions by strengthening relevant foreground responses while simultaneously suppressing non-relevant background noise and interference caused by uneven lighting. [Results] The performance of the proposed improved DeepLabV3+ model was rigorously validated on a comprehensive self-built dataset consisting of water conveyance tunnel leakage images. Validation results demonstrate that the model achieves highly satisfactory performance across key evaluation metrics. Specifically, the model obtained a Mean Intersection over Union (MIoU) of 91.98%, a specific Leakage Intersection over Union (Leak-IoU) of 86.48%, and a mean Precision (mPrecision) of 96.41%. Crucially, despite maintaining high segmentation accuracy, the model parameters are reduced to only 5.68 M,The actual FPS measured for the edge device is 12.20 f∙s^-1. In comparative analysis with the original DeepLabV3+ base model and several other mainstream semantic segmentation models, the quantitative data indicates that the proposed method significantly lowers model complexity (in terms of parameters and floating-point operations) while simultaneously achieving or exceeding the high-precision segmentation performance of much larger networks.[Conclusions] In conclusion, the experimental results demonstrate that the improved lightweight DeepLabV3+ semantic segmentation model striking an optimal balance between accuracy and efficiency. By integrating MobileNetV3, the cascaded context aggregation module, and the scSE attention mechanism, the model overcomes common environmental challenges in tunnels to provide robust segmentation of irregular and blurred leakage areas. The specific lightweight design of this model aligns directly with the practical requirements for field-based edge computing deployment during tunnel inspection routines. Therefore, this research offers a reliable, advanced, and practical technical support system for the intelligent identification of water leakage and the digitized engineering operation and maintenance (O&M) of water conveyance tunnels.