A tunnel structural health monitoring solution using computer vision and data fusion

Abstract

Tunnel structural health monitoring is predominantly done through periodic visual inspections, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. Drawbacks associated with this include the subjectivity of the surveys and, most of the time, the shutting down of operations during the inspection. To mitigate these, an increasing effort was made to automate inspections using robotics to reduce human presence and computer vision techniques to detect defects along tunnel linings. While defect identification is beneficial, comprehensive monitoring to identify changes on tunnel linings can provide a more informative survey to further automate inspection and analysis. CERN, the European Organisation for Nuclear Research has more than 50 km of tunnels which need monitoring. This raised the need for a remotely operated surveying system to monitor the structural health of the tunnels. Hence, a tunnel inspection solution to monitor for changes on tunnel linings is proposed here. Using a robotic platform hosting a set of cameras, tunnel wall images are automatically and remotely captured. The tunnel environment poses a number of challenges, with two of these being different light conditions and reflections on metallic objects. To alleviate this, pre-processing stages were developed to correct for the uneven illumination and to localise highlights. Crack detection using deep learning techniques is employed following the pre-processing stages to identify cracks on concrete walls. A change detection process is implemented through a combination of different bi-temporal pixel-based fusion methods and decision-level fusion of change maps. The evaluation of the proposed solution is made through qualitative analysis of the resulting change maps followed by a quantitative comparison with ground-truth changes. High recall and precision values of 81% and 93% were respectively achieved. The proposed solution provides a better means of structural health monitoring where data acquisition is carried out on-site during shutdowns or short, infrequent maintenance periods and post-processed off-site.