Open AccessDetection of Surface Defects of Steel Plate Based on ViT
Author(s) -
Jiangling Fan,
Xufeng Ling,
JingXin Liang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2002/1/012039
Subject(s) - transformer , artificial intelligence , paragraph , sentence , surface (topology) , computer science , sequence (biology) , word (group theory) , computer vision , pattern recognition (psychology) , materials science , mathematics , engineering , chemistry , voltage , geometry , electrical engineering , biochemistry , world wide web
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.