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http://dx.doi.org/10.5407/jksv.2021.19.1.068

Rubber O-ring defect detection system using K-fold cross validation and support vector machine  

Lee, Yong Eun (School of Mechanical Engineering, Pusan National University)
Choi, Nak Joon (ICT Co., LTD)
Byun, Young Hoo (Dae Young High-Chem Co., LTD)
Kim, Dae Won (Intown Co., LTD)
Kim, Kyung Chun (School of Mechanical Engineering, Pusan National University)
Publication Information
Journal of the Korean Society of Visualization / v.19, no.1, 2021 , pp. 68-73 More about this Journal
Abstract
In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.
Keywords
Defect Detection; Data Augmentation; Support Vector Machine; Cross Validation;
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