Research on steel surface defect classification method based on deep learning

Research on steel surface defect classification method based on deep learning

Blog Article

Abstract Surface defects on steel, arising from factors like steel composition and manufacturing techniques, pose significant challenges to industrial production.Efficient and Main generic frames in the media coverage of environmental popular consultations in Colombia. precise detection of these defects is crucial for enhancing production efficiency and product quality.In accordance with these requisites, this paper elects to undertake the detection task predicated on the you only look once (YOLO) algorithm.In this study, we propose a novel approach for surface flaw identification based on the YOLOv5 algorithm, called YOLOv5-KBS.This method integrates attention mechanism and weighted Bidirectional Feature Pyramid Network (BiFPN) into YOLOv5 architecture.

Our method addresses issues of background interference and defect size variability in images.Experimental results show that the YOLOv5-KBS model achieves a notable 4.2% increase in mean Average Precision (mAP) and reaches a detection speed of 70 Frames Per Flexible and wearable battery-free backscatter wireless communication system for colour imaging Second (FPS), outperforming the baseline model.These findings underscore the effectiveness and potential applications of our proposed method in industrial settings.