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A multi-target bird recognition method for transmission lines based on radar and camera data fusion
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Chengtao FAN1, Wei GAO1, Xiaoxi JIN2
Electrical Engineering | 2025, 26(6) : 29 - 37
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Electrical Engineering | 2025, 26(6): 29-37
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A multi-target bird recognition method for transmission lines based on radar and camera data fusion
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Chengtao FAN1, Wei GAO1, Xiaoxi JIN2
Affiliations
  • 1 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108
  • 2 Fuzhou Electric Power Design Institute Co., Ltd, Fuzhou 350007
Published: 2025-06-15
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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.

identify and repel birds  /  visual images  /  radar radio frequency images  /  sensor fusion  /  deep convolutional neural networks
Chengtao FAN, Wei GAO, Xiaoxi JIN. A multi-target bird recognition method for transmission lines based on radar and camera data fusion[J]. Electrical Engineering, 2025 , 26 (6) : 29 -37 .
Year 2025 volume 26 Issue 6
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Article Info
  • Receive Date:2025-01-09
  • Online Date:2025-10-30
  • Published:2025-06-15
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History
  • Received:2025-01-09
  • Revised:2025-02-25
Affiliations
    1 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108
    2 Fuzhou Electric Power Design Institute Co., Ltd, Fuzhou 350007
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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