Novel visual crack width measurement based on backbone double-scale features for improved detection automation

Tang, Yunchao; Huang, Zhaofeng; Chen, Zhang; Chen, Mingyou; Zhou, Hao; Zhang, Hexin; Sun, Junbo

Abstract

State-of-the-art machine-vision systems have limitations associated with crack width measurements. The sample points used to describe the crack width are often subjectively defined by experimenters, which obscures the crack width ground truth. Consequently, in most related studies, the uncontrollable system errors of vision modules result in unsatisfactory measurement accuracy. In this study, the cracks of a reservoir dam are taken as objects, and a new crack backbone refinement algorithm and width-measurement scheme are proposed. The algorithm simplifies the redundant data in the crack image and improves the efficiency of crack-shape estimation. Further,
an effective definition of crack width is proposed that combines the macroscale and microscale characteristics of the backbone to obtain accurate and objective sample points for width description. Compared with classic methods, the average simplification rate of the crack backbone and the average error rate of direction judgment are all improved. The results of a series of experiments validate the efficacy of the proposed method by showing that it can improve detection automation and has potential engineering application.

• Journal Article Type: Article
• Acceptance Date: Oct 19, 2022
• Online Publication Date: Oct 27, 2022
• Publication Date: 2023-01
• Deposit Date: Oct 28, 2022
• Publicly Available Date: Oct 28, 2023
• Print ISSN: 0141-0296
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 274
• Article Number: 115158
• DOI: https://doi.org/10.1016/j.engstruct.2022.115158
• Keywords: Concrete crack, Image thinning, Machine vision, Multi-scale feature fusion
• Public URL: http://researchrepository.napier.ac.uk/Output/2936528