隧道作为现代交通基础设施的核心组成部分，其结构安全直接关系到交通运营与公众安全。渗漏水作为隧道最常见的病害之一，若不及时检测与治理，将导致结构腐蚀、强度衰减甚至坍塌等严重后果。然而，隧道环境中的复杂性，如光照不均导致的目标与背景对比度低、渗漏水形态多变引发的特征捕捉困难等，对目标检测结果的准确性和有效性带来了很大的挑战。针对上述问题，该文提出基于改进 YOLOv10 的隧道渗漏水智能检测方法LPM-YOLO。首先，文中提出了一种局部-全局注意力分支融合模块LGAFM，实现多尺度特征的自适应加权融合；其次，通过引入基于令牌统计自注意力机制的PTSSA模块，摒弃传统自注意力的二次复杂度计算，将计算复杂度降至线性水平，有效提升长距离依赖建模效率；最后，基于Mamba Vision混合架构思想，在主干网络中引入C2f_MV模块，提升了模型捕捉长程空间依赖关系的能力。实验表明，LPM-YOLO 在保持轻量化特性的同时，实现了75.6%的AP₅₀和41.7%的AP_50:95，较原始 YOLOv10-s分别提升6.7%和2.5%。

Tunnels, as a core component of modern transportation infrastructure, play a critical role in ensuring the safety of transportation operations and public safety. Water leakage, one of the most common issues in tunnels, can lead to severe consequences such as structural corrosion, strength degradation, or even collapse if not detected and ad-dressed promptly. However, the complexity of the tunnel environment and the varia-bility of water leakage patterns pose significant challenges to the accuracy and effec-tiveness of target detection results. To address these issues, this paper proposes an in-telligent water leakage detection method for tunnels based on an improved YOLOv10, LPM-YOLO. First, this paper proposes a Local-Global Attention Fusion Module (LGAFM) to achieve adaptive weighted fusion of multi-scale features. Second, by in-troducing the PTSSA module based on Token-Statistical Self-Attention, the traditional quadratic complexity of self-attention calculations is eliminated, and the efficiency of modeling long-range dependencies is effectively improved. Finally, based on the Mamba Vision hybrid architecture concept, the C2f_MV module is introduced to en-hance the model's ability to capture long-range spatial dependencies. Experiments demonstrate that LPM-YOLO achieves 75.6% AP₅₀ and 41.7% AP_50:95 while maintaining its lightweight characteristics, representing improvements of 6.7% and 2.5% over the original YOLOv10-s, respectively.