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About Segmentation-Based Deep-Learning Approach For Surface-Defect Detection
This paper presents a segmentation-based deep learning approach for surface defect detection. The proposed model uses a small number (25-30) of defective training samples, which is practical for industry where available defective samples are limited. Experiments on a new surface defect dataset show the model outperforms related methods, including commercial software, while only requiring a small number of training samples. The paper evaluates factors like required annotation precision and computational cost to assess the method's suitability for practical applications in quality control.
Detailed Information
| Author: | ['bruce wang'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 17 |
| Language: | English |
| Format: | |
| Price: | FREE |
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