Authors:
Toqa Alaa
1
;
Mostafa Kotb
1
;
Arwa Zakaria
1
;
Mariam Diab
1
and
Walid Gomaa
2
;
1
Affiliations:
1
Department of Computer Science and Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt
;
2
Faculty of Engineering, Alexandria University, Alexandria, Egypt
Keyword(s):
Vision Transformers, Classification, Localization, Convolution Neural Networks, GC10-DET, NEU-DET, Multi-DET.
Abstract:
Metal manufacturing often results in the production of defective products, leading to operational challenges. Since traditional manual inspection is time-consuming and resource-intensive, automatic solutions are needed. The study utilizes deep learning techniques to develop a model for detecting metal surface defects using Vision Transformers (ViTs). The proposed model focuses on the classification and localization of defects using a ViT for feature extraction. The architecture branches into two paths: classification and localization. The model must approach high classification accuracy while keeping the Mean Square Error (MSE) and Mean Absolute Error (MAE) as low as possible in the localization process. Experimental results show that it can be utilized in the process of automated defects detection, improve operational efficiency, and reduce errors in metal manufacturing.