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Image-based corrosion recognition for ship steel structures
Author(s): Yucong Ma; Yang Yang; Yuan Yao; Shengyuan Li; Xuefeng Zhao
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Paper Abstract

Ship structures are subjected to corrosion inevitably in service. Existed image-based methods are influenced by the noises in images because they recognize corrosion by extracting features. In this paper, a novel method of image-based corrosion recognition for ship steel structures is proposed. The method utilizes convolutional neural networks (CNN) and will not be affected by noises in images. A CNN used to recognize corrosion was designed through fine-turning an existing CNN architecture and trained by datasets built using lots of images. Combining the trained CNN classifier with a sliding window technique, the corrosion zone in an image can be recognized.

Paper Details

Date Published: 27 March 2018
PDF: 7 pages
Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 106020U (27 March 2018); doi: 10.1117/12.2296540
Show Author Affiliations
Yucong Ma, Dalian Univ. of Technology (China)
Yang Yang, Dalian Univ. of Technology (China)
Yuan Yao, Dalian Univ. of Technology (China)
Shengyuan Li, Dalian Univ. of Technology (China)
Xuefeng Zhao, Dalian Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10602:
Smart Structures and NDE for Industry 4.0
Norbert G. Meyendorf; Dan J. Clingman, Editor(s)

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