Parametric Performance Evaluation of Wavelet-Based Corrosion Detection Algorithms for Condition Assessment of Civil Infrastructure Systems

Abstract

Corrosion is a crucial defect in structural systems that can lead to catastrophic effects if neglected. Current structure inspection standards require an inspector to visually assess the conditions of a target structure. A less time-consuming and inexpensive alternative to current monitoring methods is to use a robotic system, which can inspect structures more frequently and perform autonomous damage detection. The feasibility of using image processing techniques to detect corrosion in structures has been acknowledged by leading experts in the field; however, there has not been a systematic study to evaluate the effects of different parameters on the performance of vision-based corrosion detection systems. This study evaluates several parameters that can affect the performance of color wavelet-based texture analysis algorithms for detecting corrosion. Furthermore, an approach is proposed to utilize the depth perception for corrosion detection. The proposed approach improves the reliability of the corrosion detection algorithm. The integration of depth perception with pattern classification algorithms, which has never been reported in published studies, is part of the contribution of the current study. Several quantitative evaluations are presented to scrutinize the performance of the investigated approaches.