LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV Aerial Imagery

Deep learning is vital for improving drone intelligence through UAV aerial object detection. However, current algorithms struggle to balance detection speed and accuracy. To address this issue, in this paper we propose a lightweight UAV aerial image object detection algorithm based on YOLOv8 named LW-YOLOv8. Firstly, the lightweight VanillaNet architecture is used in the backbone network to effectively reduce the number of YOLOv8 network parameters. Then, a feature pyramid network AFPNs based on shallow network fusion is proposed to improve the detection performance of small targets in UAV aerial images. Experimental results on the VisDrone2019 dataset show that compared with the YOLOv8 algorithm, the proposed algorithm reduces the number of parameters and improves the detection accuracy simultaneously.