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In some industrial scenarios, insufficient defect samples and labeling time-consuming and labor-intensive defects, limit the application of machine vision methods in surface defect detection. Technologies of industrial defect detection based on few-shot learning were introduced from three aspects: image acquisition, image processing, and defect detection. Firstly, defect detection methods were divided into traditional surface defect detection methods and few-shot deep learning based defect detection methods. The traditional surface defect detection method was based on the manually extracted features to identify defects, which could be divided into three parts: defect segmentation, artificial feature extraction and defect recognition. Few-shot deep learning based industrial defect detection methods include data enhancement, transfer learning, model fine-tuning, semi-supervised learning, weakly supervised learning, unsupervised learning methods, etc. Secondly, some commonly used defect detection datasets and evaluation criteria of detection results were introduced. Finally, the existing problems and future research directions of few-shot learning based surface defect detection were discussed.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023239
China Classification Code:TP391.41
Citation Information:
[1]CHEN Li,YIN Xiangting,JIN Qifan ,et al.Review of Surface Defect Detection Methods Based on Few-shot Learning[J].Journal of Zhengzhou University(Natural Science Edition),2025,57(03):1-11.DOI:10.13705/j.issn.1671-6841.2023239.
Fund Information:
国家自然科学基金项目(62202433,U21B2037,62272422,62172371,U22B2051); 国家重点研发计划项目(YFB3301504); 河南省博士后基金项目(202103111); 河南省自然科学基金项目(22100002)
2024-03-15
2024-03-15
2024-03-15