A detection and classification approach for underwater dam cracks. (September 2016)

• Record Type: Journal Article
• Authors: Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Wang, Gengren
• Is Part Of: Structural health monitoring. Volume 15:Number 5(2016)
• Page Start: 541
• Page End: 554
• Publication Date: 2016-09
• Subjects: Underwater dam -- crack detection -- crack classification -- image processing -- evidence theory; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921716651039
• Languages: English
• ISSNs: 1475-9217

Abstract

Underwater dam crack detection and classification based on visible images is a challenging task. The underwater environment is very complex with uneven illumination and serious noise problems, which often leads to the distortion of detection. In addition, there are few methods suitable for underwater dam crack classification. To solve these problems, a novel underwater dam crack detection and classification approach is proposed. Firstly, a dodging algorithm is used to eliminate the uneven illumination in the underwater visible images. Subsequently, a crack detection approach is proposed, where the local characteristics of image blocks and the global characteristics of connected domains are both used based on the analysis of the statistical properties of dam crack images. Finally, an improved evidence theory-based crack classification algorithm is proposed after the crack detection. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively in complex underwater environments.