A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

Abstract

Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registration, edge detection, line detection, morphological functions, colour analysis, texture detection, wavelet transform, segmentation, clustering and pattern recognition, are key pieces that could be merged to solve this problem. Missing or deformed structural members, cracks and corrosion are main deterioration measures that are found in structures, and they are the main examples of structural deterioration considered here. This paper provides a survey and an evaluation of some of the promising vision-based approaches for automatic detection of missing (deformed) structural members, cracks and corrosion in civil infrastructure systems. Several examples (based on laboratory studies by the authors) are presented in the paper to illustrate the utility, as well as the limitations, of the leading approaches.