Improved faster R-CNN for steel surface defect detection in industrial quality control
Abstract Steel surface defect detection constitutes a critical inspection task in industrial production. To address challenges including missed detections and low accuracy for fine defects, this study develops an enhanced Faster R-CNN algorithm. The proposed framework incorporates a feature fusion m...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12740-x |
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| Summary: | Abstract Steel surface defect detection constitutes a critical inspection task in industrial production. To address challenges including missed detections and low accuracy for fine defects, this study develops an enhanced Faster R-CNN algorithm. The proposed framework incorporates a feature fusion module and lightweight channel attention mechanism between Feature Pyramid Networks (FPN) and Region Proposal Network (RPN), substantially augmenting subtle feature extraction capabilities. Evaluated on the NEU-DET dataset, the optimized model achieves a mean average precision (mAP) of 80.2%-yielding a 12.6% improvement over the baseline-while increasing detection speed by 40.9%. This approach not only significantly elevates defect recognition accuracy but also establishes a practical framework for automated steel surface inspection systems. |
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| ISSN: | 2045-2322 |