Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

TLDR

Computer vision with remote cameras and UAVs offers promising non‑contact solutions for assessing civil infrastructure, though key challenges remain for fully automated vision‑based inspection and monitoring. The paper reviews recent computer vision advances for civil infrastructure assessment, aiming to automatically convert image or video data into actionable information and to address remaining challenges. The review classifies recent work into inspection and monitoring applications, covering component identification, damage characterization, change detection, static strain and displacement measurement, and dynamic displacement for modal analysis.

Abstract

Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.