Image-based corrosion recognition for ship steel structures

Yucong Ma; Yang Yang; Yuan Yao; Shengyuan Li; Xuefeng Zhao

doi:10.1117/12.2296540

27 March 2018 Image-based corrosion recognition for ship steel structures

Yucong Ma, Yang Yang, Yuan Yao, Shengyuan Li, Xuefeng Zhao

Proceedings Volume 10602, Smart Structures and NDE for Industry 4.0; 106020U (2018) https://doi.org/10.1117/12.2296540
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2018, Denver, Colorado, United States

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.

Citation Download Citation

Yucong Ma, Yang Yang, Yuan Yao, Shengyuan Li, and Xuefeng Zhao "Image-based corrosion recognition for ship steel structures", Proc. SPIE 10602, Smart Structures and NDE for Industry 4.0, 106020U (27 March 2018); https://doi.org/10.1117/12.2296540

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