A review of surface defect detection for metal materials based on deep learning

LI Zongyou; GAO Chunyan; LV Xiaoling; ZHANG Minglu

doi:10.19287/j.mtmt.1005-2402.2023.06.011

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Article Navigation > Manufacturing Technology & Machine Tool > 2023 > (6): 61-67

LI Zongyou, GAO Chunyan, LV Xiaoling, ZHANG Minglu. A review of surface defect detection for metal materials based on deep learning[J]. Manufacturing Technology & Machine Tool, 2023, (6): 61-67. doi: 10.19287/j.mtmt.1005-2402.2023.06.011

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LI Zongyou, GAO Chunyan, LV Xiaoling, ZHANG Minglu. A review of surface defect detection for metal materials based on deep learning[J]. Manufacturing Technology & Machine Tool, 2023, (6): 61-67. doi: 10.19287/j.mtmt.1005-2402.2023.06.011

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A review of surface defect detection for metal materials based on deep learning

doi: 10.19287/j.mtmt.1005-2402.2023.06.011

School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, CHN

Received Date: 2023-01-17
Accepted Date: 2023-03-13

Abstract

Abstract

Surface defect detection is a key part of product quality inspection, and with the rapid development of deep learning technology in recent years, the surface defect detection technology of metal materials has been greatly improved. This paper compares and analyzes the surface defect detection methods of metal materials based on deep learning in recent years, and discusses the status of research and application effects in recent years from three aspects: supervised methods, unsupervised methods, and weakly supervised methods. Finally, the key problems and solutions in the detection of surface defects in metal materials are systematically summarized. The further development of surface defect detection is considered and foreseen in the light of industrial needs.
- surface defect detection,
- deep learning,
- convolutional neural network,
- metal materials,
- image processing

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References(47)

References

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A review of surface defect detection for metal materials based on deep learning

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