A novel underwater dam crack detection and classification approach based on sonar images

• Published in: PloS one Vol. 12; no. 6; p. e0179627
• Main Authors: Shi, Pengfei, Fan, Xinnan, Ni, Jianjun, Khan, Zubair, Li, Min
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22-06-2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Classification; Cluster analysis; Clustering; Cracks; Dams; Engineering; Engineering and Technology; Fragmentation; Fragments; Fuzzy Logic; Image classification; Image detection; Image Processing, Computer-Assisted - methods; Image resolution; Immersion; Methods; Neural networks; Nondestructive testing; Physical Sciences; Reasoning; Research and Analysis Methods; Social Sciences; Sonar; Sonar imagery; Sound; Tomography; Trees; Voting
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0179627

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.