基于深度学习的隧道衬砌渗漏水智能识别

Intelligent Identification of Tunnel Lining Water Leakage Based on Deep Learning

隧道衬砌渗漏水的准确识别对于保障隧道的安全运营具有重要意义。然而,传统的渗漏水识别方法存在主观性大、效率低下等不足,无法满足复杂场景和多变的隧道情况。为了解决该问题,本文提出了一种基于深度学习的智能识别方法。该方法首先以Unet语义分割算法为基础搭建网络模型,然后利用现场采集的渗漏水数据集进行模型训练,最后通过精度评估指标mPA和mIoU对模型性能进行评估。实验结果表明,该方法在隧道衬砌渗漏水的分割任务上表现良好,模型评估指标mPA达到了93.71%,mIoU达到了86.89%,能够准确地分割出渗漏水区域与背景,适用于实际隧道工程的衬砌渗漏水智能检测任务。

The identification of tunnel lining water leakage is of great importance for the safe operation of tunnels. However, the traditional water leakage identification method suffers from the deficiencies of subjectivity and low efficiency, which cannot meet the complex scenarios and changing tunnel situations. To solve the problem, an intelligent identification method for water leakage based on deep learning is proposed in this paper. The method first builds a network model based on the Unet semantic segmentation algorithm, then uses the water leakage dataset collected in the field for model training, and finally evaluates the model performance by accuracy evaluation indexes mPA and mIoU. The experimental results show that the method performs well in the task of tunnel lining water leakage segmentation, with the model evaluation indexes mPA reaching 93.71% and mIoU reaching 86.89%, which can accurately segment the water leakage area and background, thus it is suitable for the task of intelligent detection of lining water leakage in actual tunnel projects.