Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges
Darragh Lydon, Rolands Kromanis, Myra Lydon, Juliana Early, Susan Taylor
Abstract
Abstract Displacement measurements can provide valuable insights into structural conditions and in-service behaviour of bridges under operational and environmental loadings. Computer vision systems have been validated as a means of displacement estimation; the research developed here is intended to form the basis of a real-time damage detection system. This paper demonstrates a solution for detecting damage to a bridge from displacement measurements using a roving vision sensor-based approach. Displacements are measured using a synchronised multi-camera vision-based measurement system. The performance of the system is evaluated in a series of controlled laboratory tests. For damage detection, five unsupervised anomaly detection techniques: Autoencoder, K-Nearest Neighbours, Kernel Density, Local Outlier Factor and Isolation Forest, are compared. The results obtained for damage detection and localisation are promising, with an f1-Score of 0.96–0.97 obtained across various analysis scenarios. The approaches proposed in this research provide a means of detecting changes to bridges using low-cost technologies requiring minimal sensor installation and reducing sources of error and allowing for rating of bridge structures.