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

ABSTRACT 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.