Abstract
The human visual inspection to find defects from welding joints is very tough. The examiners have to inspect many bend test fragments carefully. The present study aims to build an automatic detection system capable of finding cracks from bend test fragments. This paper describes the automatic detection method employing Faster R-CNN to detect crack regions. First, we introduce our achievement and explain the focused issue. Second, the structure of the proposed Faster R-CNN is explained, and then the present paper shows the experiment of automatic detection using web-camera working in real-time. Finally, conclusions and future works are discussed.
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Acknowledgments
The authors would like to thank Crimson Interactive Pvt. Ltd. (Ulatus) - www.ulatus.jp for their assistance in manuscript translation and editing, and Ueno in MathWorks for technical advice. This work was supported by a Grant-in-Aid from JWES (The Japan Welding Engineering Society).
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Kato, S., Hino, T., Kumeno, H., Kume, S., Kagawa, T., Nobuhara, H. (2022). Fault Detection from Bend Test Images of Welding Using Faster R-CNN. In: Barolli, L., Kulla, E., Ikeda, M. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-95903-6_21
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