A novel approach for defect detection on vessel structures using saliency-related features. (1st February 2018)

• Record Type: Journal Article
• Authors: Bonnin-Pascual, Francisco; Ortiz, Alberto
• Is Part Of: Ocean engineering. Volume 149(2018)
• Page Start: 397
• Page End: 408
• Publication Date: 2018-02-01
• Subjects: Defect detection -- Vessel inspection -- Corrosion -- Cracks -- Saliency -- Micro-aerial vehicle; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2017.08.024
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Seagoing vessels have to undergo regular visual inspections in order to detect defects such as cracks and corrosion before they result into catastrophic consequences. These inspections are currently performed manually by ship surveyors at a great cost, so that any level of assistance during the inspection process by means of e.g. a fleet of robots capable of defect detection would significatively decrease the inspection cost. In this paper, we describe a novel framework for visually detecting the aforementioned defects. This framework is generic and flexible in the sense that it can be easily configured to compute the features that perform better for the inspection at hand. Making use of this framework and inspired by the idea of conspicuity, this work considers contrast and symmetry as features for detecting defects and shows their usefulness for the case of vessels. Three different combination operators are additionally tested in order to merge the information provided by these features and improve the detection performance. Experimental results for different configurations of the detection framework show better classification rates than state of the art methods and prove its usability for images collected by a micro-aerial robotic platform intended for visual inspection. Highlights: Detection of cracks and corrosion on images taken from vessel structures. Learning-free method implemented as a multi-stage reconfigurable generic framework. Contrast and symmetry selected as features for detect defection on vessels. Better classification rates than state of the art methods. Usability proved with images taken from different areas and using an aerial robot.