为提高隧道渗漏水检测过程中的效率和精度，降低渗漏水检测的错检、漏检概率，避免人工检测方式导致的因人员工作水平及判断主观性对检测结果的不利影响，同时考虑隧道内部低光照条件对成像效果和图像识别分割的影响，引入Google团队提出的EfficientNet-B1网络替换U-Net中编码器部分的主干网络，提出一种基于U-Net模型改进的隧道渗漏水检测识别分割方法。引入门控机制替换了原U-Net模型中的concat拼接，并在门控模块中集成SE（squeeze-and-excitation）模块。引入FCN网络，U-Net与基于原U-Net网络改进后的网络模型进行实验对比。结果表明：原U-Net网络的mIoU为75.08%，平均Dice系数为0.847，平均Hausdor距离为5.01，引入结合SE注意力模块的门控机制替代原U-Net网络跳跃连接中的concat拼接后，其mIoU为78.92%，平均Dice系数为0.877，平均Hausdor距离为4.93，使用EfficientNet-B1网络替换U-Net中编码器部分的主干网络后，其mIoU为84.24%，平均Dice系数为0.914，平均Hausdor距离为3.792，均为所有网络中最优值。改进网络的mIoU较U-Net，全卷积神经网络（fully convolutional networks, FCN）分别提高了9.16%，4.32%，该方法不仅提升了模型的识别分割能力，同时使得模型对于红外渗漏图像识别效果得到显著增强。

To enhance the efficiency and accuracy of tunnel leakage detection, reduce false and missed detection rates, and mitigate the impact of subjective judgment and varying expertise in manual detection methods, a novel segmentation approach based on an improved U-Net model was proposed. This method addressed the challenges posed by low-light conditions within tunnels, which negatively affected both imaging quality and segmentation performance. The EfficientNet-B1 network, developed by Google, was used as the encoder backbone for the U-Net model. Furthermore, a gated mechanism was employed to replace the traditional concatenation operation in the U-Net skip connections, and an squeeze-and-excitation (SE) module was incorporated into the gating mechanism. Comparative experiments were performed between the improved model, the original U-Net, and FCN models. The results show that the original U-Net achieves a mean intersection over union(mIoU) of 75.08%, an average Dice coefficient of 0.847, and an average Hausdor distance of 5.01. By replacing the skip connection concatenation with the SE-enhanced gating mechanism, the mIoU is increased to 78.92%, the average Dice coefficient improves to 0.877, and the Hausdor distance decreases to 4.93. The integration of EfficientNet-B1 as the U-Net encoder further boosts the mIoU to 84.24%, the average Dice coefficient to 0.914, and the Hausdor distance is reduced to 3.792, achieving the best performance among all models. Overall, a 9.16% improvement in mIoU is achieved compared to the original U-Net, and a 4.32% improvement is noted compared to the fully convolutional networks(FCN) model. These results demonstrate that the proposed method not only enhances the model's segmentation and recognition capabilities but also significantly improves its performance in identifying infrared leakage images.