Mr. Yong Wang Profile

Yong Wang

at Beijing Urban Construction Exploration & Surveying

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Proceedings Article | 10 May 2023 Paper

Metro tunnel disease detection based on point cloud data and a mask R-CNN framework

Li Wang, Chao Tang, Jianping Chen, Tingli Fan, Yang Li, Yong Wang

Proceedings Volume 12554, 125540C (2023) https://doi.org/10.1117/12.2651834

KEYWORDS: Image segmentation, Image processing, Defect detection

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The automatic detection of subway tunnel lining disease is realised primarily by using industrial cameras and deep learning. However, due to the uniqueness of subway tunnel environments, industrial cameras can be subject to excess interference and most of the methods are not based on disease characteristics, leaving much room for improvement. This paper proposes a method using point cloud data and a mask region-based convolutional neural network. Using two types of diseases— that is, lining water leakage and dropblocks—as the research objects, the reflection intensity and three-dimensional space information of laser point cloud data were respectively used to generate grayscale and depth maps. Based on the MASK R-CNN learning framework, the proposed method realises the simultaneous detection of two types of diseases, and performs pixel-by-pixel analysis on the feature map through the mask branch to generate a binarised mask. Experiments showed that the disease identification method proposed in this paper could achieve a global accuracy rate of more than 95%, the mAP index value being 0.4. The prediction boxes obtained could cover a more complete disease area. The proposed method combined the advantages of a grayscale map, depth map and mask R-CNN network to achieve simultaneous object detection and instance segmentation for water leakage and drop blocks, and achieved high recognition accuracy and excellent mask results—that is, the area value of the mask could be used as the basis for judging the severity of the disease, providing a certain reference for subsequent maintenance and actual operational application.

Proceedings Article | 5 November 2020 Paper

Studies on automatic extraction of precise 3D laser measurement data in specific section of subway tunnel

Chao Tang, Haiqian Hou, Tingli Fan, Lifeng Zhao, Yong Wang, Zihao Li, Xiaojing Wang

Proceedings Volume 11562, 1156212 (2020) https://doi.org/10.1117/12.2580108

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In the construction period and operation period of an urban subway, due to the comprehensive impact of geology, groundwater, the construction of adjacent foundation pits and the load of its own structure, the tunnel structure of the subway may go through deformation and other changes that endanger the safety of the tunnel. With the growing scales of subways and tunnels projects, structure monitoring becomes increasingly important and a fast and accurate extraction method is critical for the monitoring of tunnel structures. In this paper, a method of extracting tunnel section based on gray map is proposed to fit the tunnel section, which is applied in a subway tunnel interval in Suzhou city. Data such as horizontal convergence value and ellipticity of a particular section in the test interval are extracted, and the results are compared with those measured by the total station measurement method at the same time. The results show that the proposed method can extract a specific section accurately and takes a short computing time. Compared to the traditional total station measurement method, the 3D laser measuring method not only improves the efficiency and data comparability of the tunnel deformation in the long-term operation monitoring, but also makes it easier for structured data to be used in the evaluation of the tunnel operation status and the development trend of the long-term deformation of a tunnel.

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