A visual recognition method for structural deformation and foreign objects in operational subway tunnels

With the development of urban underground spaces, ensuring the safety of operational subway tunnels poses significant challenges. Among these challenges, tunnel structural deformation and foreign object detection are crucial components of safety protection. Visual measurement offers specific advantages such as non-contact operation, all-weather functionality, high precision, and real-time dynamics. This paper proposes a method based on computer vision for detecting structural deformation and foreign objects in operational subway tunnels. Firstly, current tunnel morphology data is obtained through monitoring cameras. Then, digital image processing techniques such as image denoising, feature segmentation, sub-pixel positioning, filtering, and displacement calculation are applied. By comparing the processed sequential data, deformation data such as subsidence changes and horizontal changes are obtained, and the presence of foreign objects is determined. Experimental results indicate that the proposed method can simultaneously identify tunnel structural deformation and foreign object intrusion. This addresses the limitations of existing technologies, which have singular monitoring functions and incomplete tunnel health assessment systems, thereby ensuring the safety of subway operations.