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- Title
Research on Edge Detection Model of Insulators and Defects Based on Improved YOLOv4-tiny.
- Authors
Li, Boqiang; Qin, Liang; Zhao, Feng; Liu, Haofeng; Yu, Jinyun; He, Min; Wang, Jing; Liu, Kaipei
- Abstract
Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high detection speed. First, in the neck network, the inverted residual module is introduced to perform feature fusion to improve the positioning ability of the insulators. Then, a high-resolution detection output head is added to the original model to enhance its ability to detect defects. Finally, the prediction boxes are post-processed to incorporate split object boxes for large-scale insulators. In an experimental evaluation, the proposed model achieved an mAP of 96.22% with a detection speed of 10.398 frames per second (FPS) on an edge computing device, which basically meets the requirements of insulator and defect detection scenarios in edge computing devices.
- Subjects
DEEP learning; OBJECT recognition (Computer vision); EDGE computing; DATA transmission systems
- Publication
Machines, 2023, Vol 11, Issue 1, p122
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines11010122