Rapid detection technology for surface water leakage in subway tunnel

doi:10.13474/j.cnki.11-2246.2022.0259

Abstract

Abstract: With the continuous increase of subway operation time and the rise of underground water level, the leakage of subway tunnel is becoming more and more serious, which has seriously affected the safe operation of subway tunnel. The traditional detection method is manual field inspection, which has low efficiency and poor accuracy. The water leakage detection method with high automation, high accuracy and high stability is the key to improve the manual detection method. Therefore, this paper proposes a deep learning method for water leakage detection using mobile laser scanning tunnels. The method consists of the following parts.①Water leakage data set is established by using the obtained tunnel lining point cloud.②Automatic leak detection is carried out by convolutional neural network based on mask region.The test results of Aoti East-Xinglong street of Nanjing metro line 2 show that the proposed method can realize automatic detection and evaluation of tunnel lining water leakage in two-dimensional plane, and provide intuitive display of water leakage information for inspectors.

Key words: subway tunnel, 3D laser, deep learning, grayscale transformation, image binarization

CLC Number:

P258

TIAN Youliang, FAN Tingli, TANG Chao. Rapid detection technology for surface water leakage in subway tunnel[J]. Bulletin of Surveying and Mapping, 2022, 0(9): 29-33.

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