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Multi-label Classification of Steel Surface Defects Using Transfer Learning and Vision Transformer

Authors :

Amirhossein Komijani¹ Farzaneh Vafaeinezhad² Javad Khoramdel³ Yasamin Borhani⁴ Esmaeil Najafi⁵

1- دانشگاه صنعتی خواجه نصیرالدین طوسی 2- دانشگاه صنعتی خواجه نصیرالدین طوسی 3- دانشگاه تربیت مدرس 4- دانشگاه صنعتی خواجه نصیرالدین طوسی 5- دانشگاه صنعتی خواجه نصیرالدین طوسی

Keywords :

Computer vision،multi-label classification،steel defect detection،deep learning

Abstract :

Steel is the most widely used metal in various industries, for instance, automotive, buildings, packaging, etc. Defect detection on steel surfaces is crucial for steel production companies. This paper proposes a multi-label classification algorithm based on deep learning methods. Several models were trained based on the Severstal dataset, namely MobileNet- V2, Xception, DenseNet121, ViT, and ResNet-50 with transfer learning. In addition, a base model is trained from scratch based on convolutional layers such as ResNet. Moreover, a ViT model is constructed according to the attention and transformer layers concept. At first, 91% for the weighted F1 score is obtained. By exploiting the weighted loss method, the weighted F1 score increased to 92%.

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