Feature optimization-guided high-precision and real-time metal surface defect detection network.

Existing computer vision-based surface defect detection techniques for metal materials typically encounter issues with defect overlap, significant differences within classes, and similarity between defect samples. These issues compromise feature extraction accuracy and result in missed and false detections. This study proposed a feature optimization-guided high-precision and real-time metal surface defect detection network (FOHR Net) to improve defect feature expressiveness.

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block.

To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production.