SAB-YOLOv5: An Improved YOLOv5 Model for Permanent Magnetic Ferrite Magnet Rotor Detection

Bo Yu, Qi Li, Wenhua Jiao (), Shiyang Zhang and Yongjun Zhu
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Bo Yu: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Qi Li: School of Computer and Communication Engineering, Naning Tech University Pujiang Institute, Naning 211112, China
Wenhua Jiao: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Shiyang Zhang: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Yongjun Zhu: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China

Mathematics, 2024, vol. 12, issue 7, 1-17

Abstract: Surface defects on the permanent magnetic ferrite magnet rotor are the primary cause for the decline in performance and safety hazards in permanent magnet motors. Machine-vision methods offer the possibility to identify defects automatically. In response to the challenges in the permanent magnetic ferrite magnet rotor, this study proposes an improved You Only Look Once (YOLO) algorithm named SAB-YOLOv5. Utilizing a line-scan camera, images capturing the complete surface of a general object are obtained, and a dataset containing surface defects is constructed. Simultaneously, an improved YOLOv5-based surface defect algorithm is introduced. Firstly, the algorithm enhances the capability to extract features at different scales by incorporating the Atrous Spatial Pyramid Pooling (ASPP) structure. Then, the fusion of features is improved by combining the tensor concatenation operation of the feature-melting network with the Bidirectional Feature Pyramid Network (BiFPN) structure. Finally, the introduction of the spatial pyramid dilated (SPD) convolutional structure into the backbone network and output end enhances the detection performance for minute defects on the target surface. In the study, the SAB-YOlOv5 algorithm shows an obvious increase from 84.2% to 98.3% in the mean average precision (mAP) compared to that of the original YOLOv5 algorithm. The results demonstrate that the data acquisition method and detection algorithm designed in this paper effectively enhance the efficiency of defect detection permanent magnetic ferrite magnet rotors.

Keywords: permanent magnetic ferrite magnet rotors; SAB-YOLOv5; YOLOv5; defect detection; deep learning; small object detection; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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