一种基于雷达和相机数据融合网络的输电线路鸟类多目标识别方法

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Abstract This paper proposes a multi-target recgnition network for birds on power transmission lines, called RVFNet, based on the fusion of radar and camera data. The network achieves high-precision recgnition of bird targets within the monitoring range by integrating radar radio frequency (RF) data with visual images. To address the semantic differences between multimodal data, the correspondence between radar RF signals and image positional information is calculated to ensure consistency in feature representation. Structurally, the network incorporates a bird posture convolutional network (BPC) to effectively fuse multimodal information, enhancing the extraction of small-target features and preserving fine details. Additionally, a feature fusion module (FFM) is introduced to integrate multimodal features, significantly improving feature interaction while maintaining low computational costs. Experimental results demonstrate that RVFNet achieves an average bird recognition accuracy of 80.18% under various weather conditions, highlighting its robustness.

Key words： identify and repel birds visual images radar radio frequency images sensor fusion deep convolutional neural networks

Received: 09 January 2025

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	Articles by authors
	FAN Chengtao
	GAO Wei
	JIN Xiaoxi

Cite this article:

FAN Chengtao,GAO Wei,JIN Xiaoxi. A multi-target bird recognition method for transmission lines based on radar and camera data fusion[J]. Electrical Engineering, 2025, 26(6): 29-37.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I6/29

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