Derinlemesine Özellik Piramit Ağı Kullanarak Yüzey Hata Tespiti

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 109 - 115, 20.10.2021

Hüseyin Üzen İlhami Sel Muammer Türkoğlu Davut Hanbay

https://doi.org/10.53070/bbd.990950

Abstract

Yüzey hata tespiti, imalat sistemlerindeki en önemli kalite kontrol bileşenlerinden biridir. Üretim sistemlerinde otomatik yüzey hata algılama yöntemlerinin uygulanması, yüksek kaliteli ürünlerin sağlanmasında önemli bir etkendir. Bu çalışmada, otomatik yüzey hata tespiti için derinlemesine ayrılabilir evrişim tabanlı Derin Özellikli Piramit Ağ (DÖPA) mimarisi geliştirilmiştir. Bu ağ mimarisinde, önceden eğitilmiş VGG19 ağ mimarisinin öğrenilmiş parametreleri kullanılmıştır. Önerilen modelin performansını test etmek için hata tespit görüntüleri içeren MT veri seti kullanılmıştır. Deneysel çalışmalarda, önerilen DÖPA mimarisi kullanılarak %86,86 F1-skor elde edilmiştir. Bu sonuçlar, önerilen modelin var olan çalışmalardan daha başarılı olduğunu göstermiştir.

Keywords

Yüzey Hata Tespiti, Derinlemesine Ayrılabilir Evrişim, Özellikli Piramit Ağı, Derin Öğrenme.

Surface Defect Detection Using Depthwise Feature Pyramid Network

Abstract

Surface defect detection is one of the most important quality control components in manufacturing systems. The application of automatic surface defect detection methods in production systems is an important factor in ensuring high-quality products. In this study, depthwise separable convolution-based Deep Feature Pyramid Network (DÖPA) architecture was developed for automatic surface defect detection. In this network architecture, the learned parameters of the pre-trained VGG19 network architecture were used. MT dataset with defect detection images was used to test the performance of the proposed model. In experimental studies, 86.86% F1-score was obtained using the proposed DOPA architecture. These results showed that the proposed model was more successful than the existing studies.

Keywords

Surface Defect Detection, Depthwise Separable Convolution, Feature Pyramid Network, Deep Learning

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