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http://dx.doi.org/10.21289/KSIC.2022.25.4.687

Comparative Study of Deep Learning Algorithm for Detection of Welding Defects in Radiographic Images  

Oh, Sang-jin (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Yun, Gwang-ho (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Lim, Chaeog (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Shin, Sung-chul (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.25, no.4_2, 2022 , pp. 687-697 More about this Journal
Abstract
An automated system is needed for the effectiveness of non-destructive testing. In order to utilize the radiographic testing data accumulated in the film, the types of welding defects were classified into 9 and the shape of defects were analyzed. Data was preprocessed to use deep learning with high performance in image classification, and a combination of one-stage/two-stage method and convolutional neural networks/Transformer backbone was compared to confirm a model suitable for welding defect detection. The combination of two-stage, which can learn step-by-step, and deep-layered CNN backbone, showed the best performance with mean average precision 0.868.
Keywords
Radiographic Testing; Welding Defect; Deep Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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