Derinlemesine Özellik Piramit Ağı Kullanarak Yüzey Hata Tespiti

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.