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http://dx.doi.org/10.12673/jant.2022.26.2.113

Highlighting Defect Pixels for Tire Band Texture Defect Classification  

Rakhmatov, Shohruh (Research Department, DeltaX)
Ko, Jaepil (Department of Computer Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of Advanced Navigation Technology / v.26, no.2, 2022 , pp. 113-118 More about this Journal
Abstract
Motivated by people highlighting important phrases while reading or taking notes we propose a neural network training method by highlighting defective pixel areas to classify effectively defect types of images with complex background textures. To verify our proposed method we apply it to the problem of classifying the defect types of tire band fabric images that are too difficult to classify. In addition we propose a backlight highlighting technique which is tailored to the tire band fabric images. Backlight highlighting images can be generated by using both the GradCAM and simple image processing. In our experiment we demonstrated that the proposed highlighting method outperforms the traditional method in the view points of both classification accuracy and training speed. It achieved up to 13.4% accuracy improvement compared to the conventional method. We also showed that the backlight highlighting technique tailored for highlighting tire band fabric images is superior to a contour highlighting technique in terms of accuracy.
Keywords
Defect detection; GradCAM; Highlighting learning strategy; Texture defect classification; Tire band texture;
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Times Cited By KSCI : 3  (Citation Analysis)
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