@article{Guo2023, 
author = {Hongyan Guo and Xiao Liang and Ke Li and Qiang Jing},
title = {Research on Tunnel Apparent Disease Monitoring Technology Based on Machine Vision},
year = {2023},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {19},
number = {5},
pages = {1633-1645},
keywords = {highway tunnel, machine vision, crack identification, disease monitoring, image processing},
url = {https://www.sciopen.com/article/10.20174/j.juse.2023.05.025},
doi = {10.20174/j.juse.2023.05.025},
abstract = {As the number of years of operation of an in-service tunnel increases, typical apparent diseases such as cracks and water leakage, occurs, affecting the safe operation of the tunnels. How to identify various diseases through effective technical means and feedback the results to the maintenance unit is particularly important. Through the investigation of tunnel disease detection technologies and methods at home and abroad, the principle of using machine vision technology to measure typical apparent diseases of tunnels is analyzed. Combined with computer technology, an intelligent identification system for typical apparent diseases of tunnels based on zoom cameras is developed to realize the appearance of tunnel linings. Intelligent identification and monitoring of diseases, and the use of calibration plates to simulate cracks and water seepage in solid tunnels. After experimental tests, the monitoring system can realize 7×24 hours real-time monitoring of tunnel diseases. Under the condition of excellent shooting environment, the recognition rate of tunnel apparent diseases can reach 95%. The minimum width of monitoring cracks is 0.2mm, and the minimum leakage area is 50 cm2. That is to say, the system can realize the identification and all-weather monitoring of multiple diseases in multiple locations and types.}
}