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Conference Papers Year : 2020

Bird Detection on Transmission Lines Based on DC-YOLO Model

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Abstract

In order to accurately detect the number of birds around the transmission line, promptly drive the birds away to ensure the normal operation of the line, a DC-YOLO model is designed. This model is based on the deep learning target detection algorithm YOLO V3 and proposes two improvements: Replacing the convolutional layer in the original network with dilated convolution to maintain a larger receptive field and higher resolution, improving the model’s accuracy for small targets; The confidence score of the detection frame is updated by calculating the scale factor, and the detection frame with a score lower than the threshold is finally removed. The NMS algorithm is optimized to improve the model’s ability to detect occluded birds. Experimental results show that the DC-YOLO model detection accuracy can reach 86.31%, which can effectively detect birds around transmission lines.
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Dates and versions

hal-03456984 , version 1 (30-11-2021)

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Attribution - CC BY 4.0

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Cong Zou, Yong-Quan Liang. Bird Detection on Transmission Lines Based on DC-YOLO Model. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.222-231, ⟨10.1007/978-3-030-46931-3_21⟩. ⟨hal-03456984⟩
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