Transactions of China Electrotechnical Society  2021, Vol. 36 Issue (22): 4837-4848    DOI: 10.19595/j.cnki.1000-6753.tces.201595
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Intelligent Detection and Parameter Adjustment Strategy of Major Electrical Equipment Based on Optimized YOLOv4
Lü Fangcheng1,2, Niu Leilei1,2, Wang Shenghui1, Xie Qing2, Wang Zihao2
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China;
2. Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense North China Electric Power University Baoding 071003 China

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Abstract  Multi-spectral imaging method based on UAV and robot is the development trend of high voltage equipment inspection, and the identification of main electrical equipment is the basis of intelligent diagnosis of its insulation state. In this paper, the train and test database of insulator, griding ring,vibration damper, transformer bushing and conductor was established. Based on YOLOv4, the Mosaic data augmentation method was optimized , which reduces the network loss by 0.7 and improves the precision to 84.3%. The influence of IoU algorithm based on bounding box regression on detection target of different scales was studied, and the training strategies of CIoU and GIoU were proposed for large and small targets respectivly. The influence of K-means and hierarchical algorithm on the clustering result of label width and high data of self-built database and on network performance is studied. Based on the training loss, precision and speed, the network parameters of YOLOv4 is optimized. The average loss of the improved model was reduced by 3% and the mean average precision increased by 0.8%. This study can be used to assess the operation state of electrical equipment in-site with multi-spectrum image.
Key wordsElectric equipment detection      YOLOv4      data augmente algrithm      intersection ouer union      K-means and hierarchical cluster     
Received: 01 December 2020     
PACS: TM852  
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