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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12740-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736291434889216 |
|---|---|
| author | Yuefeng Leng Jiazhi Liu |
| author_facet | Yuefeng Leng Jiazhi Liu |
| author_sort | Yuefeng Leng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-089194eaf05b4467b4b7e5c3572e97a3 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-089194eaf05b4467b4b7e5c3572e97a32025-08-20T03:07:20ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-12740-xImproved faster R-CNN for steel surface defect detection in industrial quality controlYuefeng Leng0Jiazhi Liu1School of Mechanical Engineering, Liaoning Technical UniversitySchool of Mechanical Engineering, Liaoning Technical UniversityAbstract 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.https://doi.org/10.1038/s41598-025-12740-xDefect detectionFeature fusionChannel attention mechanismImproved faster R-CNN algorithm |
| spellingShingle | Yuefeng Leng Jiazhi Liu Improved faster R-CNN for steel surface defect detection in industrial quality control Scientific Reports Defect detection Feature fusion Channel attention mechanism Improved faster R-CNN algorithm |
| title | Improved faster R-CNN for steel surface defect detection in industrial quality control |
| title_full | Improved faster R-CNN for steel surface defect detection in industrial quality control |
| title_fullStr | Improved faster R-CNN for steel surface defect detection in industrial quality control |
| title_full_unstemmed | Improved faster R-CNN for steel surface defect detection in industrial quality control |
| title_short | Improved faster R-CNN for steel surface defect detection in industrial quality control |
| title_sort | improved faster r cnn for steel surface defect detection in industrial quality control |
| topic | Defect detection Feature fusion Channel attention mechanism Improved faster R-CNN algorithm |
| url | https://doi.org/10.1038/s41598-025-12740-x |
| work_keys_str_mv | AT yuefengleng improvedfasterrcnnforsteelsurfacedefectdetectioninindustrialqualitycontrol AT jiazhiliu improvedfasterrcnnforsteelsurfacedefectdetectioninindustrialqualitycontrol |