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- W3217631799 abstract "In order to ensure the smooth operation of power system, intelligent monitoring of transmission lines is necessary. At present, there are few public training pictures in the electric power industry, and the detection accuracy is not good. In this paper, a transmission line fault detection method based on YOLOv3 is proposed. This method uses YOLOv3 model and makes corresponding improvements to the network: 1. Collect and simulate foreign body suspension faults of transmission lines in multiple scenarios and sort them into training pictures. 2. Use the regression loss function GIoU to improve the positioning accuracy of the model. 3. Redesign anchor size by using k-means ++ clustering algorithm to accelerate the reasoning process and further improve network detection performance. Through experiments, the average accuracy of the proposed algorithm in transmission line foreign body detection can reach 98%, and the average detection time of the improved network is 0.025s, which is better than the YOLOv3 algorithm in both accuracy and time." @default.
- W3217631799 created "2021-12-06" @default.
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- W3217631799 creator A5045020353 @default.
- W3217631799 date "2021-11-29" @default.
- W3217631799 modified "2023-10-16" @default.
- W3217631799 title "Transmission line fault detection based on YOLOv3" @default.
- W3217631799 doi "https://doi.org/10.1117/12.2620412" @default.
- W3217631799 hasPublicationYear "2021" @default.
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