School of Rail Transit, Soochow University, Suzhou 215006, China.
School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel). 2020 Sep 1;20(17):4945. doi: 10.3390/s20174945.
The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring.
隧道结构的健康监测对于铁路运输系统的安全运行至关重要。随着隧道里程的增加，隧道结构迫切需要定期检查和健康监测，特别是有关变形和损坏的信息。然而，传统的隧道检查方法既耗时又昂贵，并且高度依赖于人的主观性。本文研究了一种基于图像数据的自动隧道监测方法，该方法通过由相机阵列组成的移动视觉测量单元进行图像数据采集。此外，还提出了几何建模和裂缝检测算法，其中利用 B 样条方法重建了稳健的三维隧道模型，并通过 Mask R-CNN 网络进行了裂缝识别。本研究的创新之处在于，我们结合了稳健的建模方法，该方法可应用于变形分析和裂缝检测，同时还采用深度学习方法基于图像传感器智能地识别隧道裂缝。在这项研究中，我们对几公里长的地铁隧道结构进行了实验，利用图像数据生成了稳健的三维模型，并自动识别了裂缝。该方法的优势在于，几何变形和裂缝损伤的综合信息可以确保健康监测的可靠性和提高准确性。
