针对渗漏水图像检测算法受隧道内复杂环境、光照强度不足、干扰物遮挡等因素的影响导致精准度不足的问题，在YOLOv8算法基础上，改进优化提出YOLO-B算法。该算法引入具备局部-全局特征协同、轻量化注意力和多尺度自适应融合的C2f_AdditiveBlock结构，提升模型在复杂环境和低光照下的适应能力；构建可跨阶段特征堆叠的Slim-Neck结构，增强对大尺度渗水扩散区域的检测能力，同时降低Neck部分的计算冗余。采用综合隧道检测车现场采集图像，结合隧道衬砌渗漏水相关公开数据集，构建复杂环境下的隧道衬砌渗漏水数据集，利用准确率、mAP50、mAP50~95和GFLOPs等作为评价指标，通过消融试验、对比分析对模型性能进行评估。结果表明： YOLO-B较原算法，mAP50、mAP50~95、准确率分别提高了1.9%、2.4%、7.2%，GFLOPs降低至7.3；与SSD、Faster R-CNN、YOLOv5、YOLOv6、YOLOv8等主流目标检测算法进行对比，mAP50分别提高6.2%、13.2%、3.2%、3.7%、1.9%，验证了所提改进算法在复杂环境衬砌渗漏水识别中的有效性。

Tunnel environments often feature complex conditions, insufficient illumination, and visual obstructions, which collectively reduce the accuracy of existing leakage-detection algorithms. To address these limitations, the authors develop an improved YOLO-B algorithm based on YOLOv8. The proposed YOLO-B model incorporates the C2f_AdditiveBlock module, which integrates local-global feature collaboration, lightweight attention, and multi-scale adaptive fusion to enhance robustness under complex and low-light conditions. A Slim-Neck module with cross-stage feature stacking is further constructed to improve detection capability for large-scale water diffusion areas while reducing computational redundancy in the neck network. Using publicly available datasets and images collected in situ by tunnel inspection vehicles, a comprehensive tunnel lining leakage dataset under realistic conditions is established. Model performance is assessed using accuracy, mAP50, mAP50－95, and GFLOPs through ablation studies and comparative analyses. The results show that: (1) relative to the baseline algorithm, YOLO-B increases mAP50 by 1.9%, mAP50－95 by 2.4%, and accuracy by 7.2%, while reducing GFLOPs to 7.3; and (2) compared with mainstream object detection algorithms such as SSD, Faster R-CNN, YOLOv5, YOLOv6, and YOLOv8, YOLO-B improves mAP50 by 6.2%, 13.2%, 3.2%, 3.7%, and 1.9%, respectively. These findings verify the effectiveness of the proposed algorithm for detecting tunnel lining leakage under complex environmental conditions.