Result Weighting-Based Resnet Feature Pyramid Network Architecture for Surface Defect Detection

Öz

Surface defect detection is very important in manufacturing systems to ensure high quality products. Unlike manual inspections under human supervision, automatic surface defect detection is both efficient and highly accurate. In this study, a Result Weighting-based Resnet Feature Pyramid Network (SA-RÖPA) model has been developed for automatic pixel-level surface defect detection. In the first stage of the proposed model, the pre-trained Resnet50 network was used, and feature maps were extracted from the different levels of this network. In the second stage, Feature Pyramid Model was applied to these feature maps in order to hierarchically share important information in defect detection. In the third stage, 4 different error detection results were obtained by using these feature maps. In the last stage, four different results obtained using the developed Result Weighting (SA) module were effectively combined. The proposed SA-ROPA model has been tested with MT, MVTex-Doku, and AITEX datasets, which are widely used in defect detection studies. In experimental studies, the mIoU value obtained for the MT and AITEX datasets using the proposed model was calculated as 79.92%, 76.37%, and 82.72%, respectively. These results have shown that the proposed SA- ROPA model is more successful than other state-of-the-art models.

Anahtar Kelimeler

• Surface Defect Detection
• Pyramid Feature Network
• Convolutional Neural Network
• Segmentation

Yüzey Hata Tespiti için Sonuç Ağırlıklandırma Tabanlı Resnet Öznitelik Piramit Ağ Mimarisi

Öz

Yüzey hata tespiti, imalat sistemlerinde yüksek kalitede ürün sağlanması açısından oldukça önemlidir. İnsan gözetimi altında yapılan manuel denetimlerin aksine, otomatik yüzey hatası tespiti hem verimli hem de yüksek doğruluktadır. Bu çalışmada piksel seviyesinde otomatik yüzey hata tespiti için Sonuç Ağırlıklandırma tabanlı Resnet Öznitelik Piramit Ağ (SA-RÖPA) modeli geliştirilmiştir. Önerilen modelin ilk aşamasında, önceden eğitilmiş Resnet50 ağı kullanılmış ve bu ağının farklı seviyelerinden öznitelik haritaları çıkartılmıştır. İkinci aşamada, hata tespitinde önemli bilgileri hiyerarşik olarak paylaşmak için bu öznitelik haritalarına Öznitelik Piramit Modeli uygulanmıştır. Üçüncü aşamada, bu öznitelik haritaları kullanılarak 4 farklı hata tespit sonucu elde edilmiştir. Son aşamada, geliştirilen Sonuç Ağırlıklandırma (SA) modülü kullanılarak elde edilen 4 farklı sonuç etkili bir şekilde birleştirilmiştir. Önerilen SA-RÖPA modeli, hata tespit çalışmalarında yaygın olarak kullanılan MT, MVTex-Doku ve AITEX veri kümeleri ile test edilmiştir. Deneysel çalışmalarda, önerilen model kullanılarak MT, MVTex-Doku ve AITEX veri kümeleri için elde edilen mIoU değeri, sırayla %79,92, %76,37 ve %82,72 olarak hesaplanmıştır. Bu sonuçlar, önerilen SA-RÖPA modelinin diğer son teknoloji modellerine göre daha başarılı olduğunu göstermiştir.

Anahtar Kelimeler

• Yüzey Hata Tespiti
• Öznitelik Piramit Ağı
• Evrişimsel Sinir Ağı
• Bölütleme

Kaynakça

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