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| Few-Shot Metric Transfer Learning Network for Surface Defect Detection |
| HUANG Jian1, ZHENG Chunhou1, ZHANG Jun2, WANG Bing3, CHEN Peng4 |
1. School of Computer Science and Technology, Anhui University, Hefei 230601;
2. School of Electrical Engineering and Automation, Anhui University, Hefei 230601;
3. School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243032;
4. School of Internet, Anhui University, Hefei 230601 |
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Abstract Metric learning method of few-shot learning is introduced into the field of defect detection, and a few-shot learning method based on transfer metric learning is proposed to meet the requirement of deep learning method for a large number of learning samples. In the first stage, the deep network is pre-trained on the large datasets which are open or easy to be obtained. In the second stage, the relevant knowledge learned by the network is transferred to the field of surface defect detection through the metric learning module.Experiments show the feasibility of few-shot learning in defect detection.
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Received: 25 February 2021
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| Fund:National Natural Science Foundation of China(No.62072002,U19A2064,61872004) |
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Corresponding Authors:
CHEN Peng, Ph.D., professor. His research interests include computer vision and data analysis.
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| About author:: HUANG Jian, master student. His research interests include computer vision and image processing.ZHENG Chunhou, Ph.D., professor. His research interests include data analysis and bioinformatics.ZHANG Jun, Ph.D., professor. His research interests include pattern recognition, intelligent information processing and bioinformatics.WANG Bing, Ph.D., professor. His research interests include intelligent information processing, computer vision and image processing. |
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2021, Vol. 34 
