UAV-Based Real-Time Object Detection Network Using Structured Pruning Strategy

Real-time object detection networks based on UAV have been used in various fields. However, some challenges need to be solved: (1) Conventional detection algorithms are not suitable for small targets; (2) The computational capacity of the UAV platform is limited; (3) The sample distribution in the aerial dataset shows the characteristics of long-tail distribution. Categories at the tail end often need to be better learned. To address these challenges, we propose the AIR-YOLO-pruned method, a lightweight UAV-based object detection method built on the YOLOv8. In this paper, we propose the AIR-YOLO which is suitable for small object detection. We introduce the gradient adaptive allocation loss to enhance the model's learning ability for tail categories. To eliminate redundant components in AIR-YOLO, we design a kind of structured pruning strategy. Experiment results indicate that our AIR-YOLOn-pruned method, with competitive computational cost, achieves a 17% improvement in accuracy compared to YOLOv8n.