基于改进YOLOv3的电力设备红外目标检测模型

doi:10.19595/j.cnki.1000-6753.tces.201324

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Abstract Infrared image detection technology is widely used in monitoring and diagnosing electrical equipment considering its non-contact and fast advantages. It is generally believed that fast and accurate localization of the equipment is the prerequisite for automatic detection and diagnosis. Compared with visible light images of ordinary objects, the infrared images of power equipment have characteristics of complex background, low contrast, similar object features, and large aspect ratio. Besides, the original YOLOv3 model is difficult to accurately locate the objects of power equipment. In view of the above problems, an improved YOLOv3 model was proposed in this paper: cross stage partial module was introduced into the backbone network; the path aggregation network was integrated into the feature pyramid structure of the original model; in addition, this study also added Mosaic data enhancement technology and CIoU loss function. The improved model was trained and tested on four types of infrared image data sets of power equipment with similar corrugated appearance structures, which showed that the detection accuracy of each type can reach more than 92%. Finally, the results were compared and evaluated with the other three mainstream object detection models. The results show that the mean average precisions of the improved model proposed in this paper were better than Faster R-CNN, SSD and YOLOv3. Although the detection speed of the improved YOLOv3 model is sacrificed compared to the original YOLOv3 model, it is significantly higher than the other two models, further verifying the effectiveness of the proposed model in this paper.

Key words： Power equipment detection YOLOv3 convolutional neural network infrared image

Received: 28 September 2020

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	Articles by authors
	Zheng Hanbo
	Li Jinheng
	Liu Yang
	Cui Yaohui
	Ping Yuan

Cite this article:

Zheng Hanbo,Li Jinheng,Liu Yang等. Infrared Object Detection Model for Power Equipment Based on Improved YOLOv3[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1389-1398.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201324 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I7/1389

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