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