Bearing defect detection based on the improved YOLOv5 algorithm

In the field of bearing defect detection, Aiming at the problem of low efficiency in manual inspection and prone to missed detections in scenarios with small target defects, and overlapping targets, an improved YOLOv5-based object detection method is proposed. Firstly, in terms of feature extraction...

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Veröffentlicht in:	PloS one 2024-10, Vol.19 (10), p.e0310007
Hauptverfasser:	Li, Kangning, Jiao, Peigang, Ding, Jiaming, DU, Weibo
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Accuracy Algorithms Analysis Bearings Bearings (Machinery) Biology and Life Sciences Computer and Information Sciences Datasets Deep learning Defects Engineering and Technology Evaluation Feature extraction Humans Image Processing, Computer-Assisted - methods Medicine and Health Sciences Modules Neural networks Object recognition Physical Sciences Product quality Quality management Research and Analysis Methods Target detection Target recognition
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