Abstract
The personnel burden is an issue with the visual inspection of welding defects that occur in bend test fragments. This study aims to construct an automatic evaluation system for welding defects that occur in bend test fragments. This paper describes the automatic detection of defective areas from bend test fragments using R-CNN. First, we have described the structure of the proposed R-CNN, followed by the experiments for evaluating R-CNN and their results. Finally, we have provided a conclusion and discussed future issues.
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The authors would like to thank Enago (www.enago.jp) for the English language review and Ueno and Maeda in MathWorks for technical advice.
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Kato, S., Hino, T., Kume, S., Nobuhara, H. (2022). Crack Detection from Weld Bend Test Images Using R-CNN. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2021. Lecture Notes in Networks and Systems, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-89899-1_31
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DOI: https://doi.org/10.1007/978-3-030-89899-1_31
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