A High Performance Air-to-Air Unmanned Aerial Vehicle Target Detection Model

In the air-to-air UAV target detection tasks, the existing algorithms suffer from low precision, low recall and high dependence on device processing power, which makes it difficult to detect UAV small targets efficiently. To solve the above problems, this paper proposes an high-precision model, ATA-YOLOv8. In this paper, we analyze the problem of UAV small target detection from the perspective of the efficient receptive field. The proposed model is evaluated using two air-to-air UAV image datasets, MOT-FLY and Det-Fly, and compared with YOLOv8n and other SOTA algorithms. The experimental results show that the mAP50 of ATA-YOLOv8 is 94.9% and 96.4% on the MOT-FLY and Det-Fly datasets, respectively, which are 25% and 5.9% higher than the mAP of YOLOv8n, while maintaining a model size of 5.1 MB. The methods in this paper improve the accuracy of UAV target detection in air-to-air scenarios. The proposed model's small size, fast speed and high accuracy make it possible for real-time air-to-air UAV detection on edge-computing devices.