Abstract: In order to overcome the shortcoming that the bird repellent is easy to be adapted by birds and to solve the problem that the target detection algorithm based on visual image is limited by weather conditions, a bird hunting and repelling method based on the fusion of radar point cloud and visual image is proposed according to the characteristics of millimeter-wave radar. Firstly, the radar-camera coordinate fusion is achieved by means of the mean fitting. Then, a bird point-cloud-image fusion dataset that covers a variety of meteorological environments is created by using vision and scene enhancement techniques. Secondly, a bird recognition model that combines the radar point-cloud attention mechanism and the deep learning recognition network YOLO is proposed to realize the fusion of decision-making layers. Finally, the intelligent start-and-stop strategy based on 3F-GIoU is constructed by combining the three-frame difference algorithm to determine whether there are birds staying in the target area, which is suitable for the target behavior recognition with small outline and fast speed. The experimental results show that the bird identification method proposed in this study can meet the robustness and accuracy of bird identification in practical application scenarios, and the average identification accuracy under different weather conditions is 91.21%. Furthermore, the 3F-GIoU strategy proposed in this study can effectively identify the activities of birds that endanger lines and towers.