Detection of Surface Defects of Steel Plate Based on ViT

A self-attention-based method termed as Vision Transformer (ViT) is applied to efficiently detect the Surface Defects of Steel Plate. The defect image is divided to N*N patches, each of which corresponds to a word, and the whole image data is used as a sentence or paragraph in NPL. A ViT framework is constructed by a learnable module with sequence length of L and 12 multi-head attention layers. We train the proposed model on the surface defects dataset. The experiment results show empirically that ViT has superior performance compared to alternative approaches.