Pavement crack analysis by referring to historical crack data based on multi-scale localization

• Published in: PloS one Vol. 15; no. 8; p. e0235171
• Main Authors: Wang, Xianglong, Zhaozheng, Hu, Li, Na, Qin, Lingqiao
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 14.08.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Asphalt pavements; Biology and Life Sciences; Computer and Information Sciences; Crack propagation; Cracks; Deep learning; Engineering and Technology; Engineering research; Flaw detection; Localization; Localization method; Machine learning; Management systems; Methods; Multiscale analysis; Multiscale methods; Neural networks; Pavement cracking; Pavement management; Pavements; Pixels; Research and Analysis Methods; Social Sciences; Urban planning; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0235171

Pavement crack analysis, which deals with crack detection and crack growth detection, is a crucial task for modern Pavement Management Systems (PMS). This paper proposed a novel approach that uses historical crack data as reference for automatic pavement crack analysis. At first, a multi-scale localization method, which including GPS based coarse localization, image-level localization, and metric localization has been presented to establish image correspondences between historical and query crack images. Then historical crack pixels can be mapped onto the query crack image, and these mapped crack pixels are seen as high-quality seed points for crack analysis. Finally, crack analysis is accomplished by applying Region Growing Method (RGM) to further detect newly grown cracks. The proposed method has been tested with the actual pavement images collected in different time. The F-measure for crack growth is 88.9%, which demonstrates the proposed method has an ability to greatly simplify and enhances crack analysis result.