ArticleScientific reports2025
Improved faster R-CNN for steel surface defect detection in industrial quality control.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A feature-enhanced transformer for point-like micro-defect detection on semiconductor wafer surfaces.Scientific reports · 2026Article
- WTCFNet for industrial defect detection using wavelet transform and cross layer feature fusion.Scientific reports · 2026Article
- YOLOv12-VSD: A Transfer-Learning-Assisted Real-Time Detection Algorithm for Vehicle Surface Defects.Sensors (Basel, Switzerland) · 2026Article
- Lightweight Visual Localization of Steel Surface Defects for Autonomous Inspection Robots Based on Improved YOLOv10n.Sensors (Basel, Switzerland) · 2026Article
- Rethinking multi-stage calibration fusion network and loss function for steel surface defect detection.Scientific reports · 2025Article
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Authors and funding
2 authors.
Funding
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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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