Abstract
Model lightweighting and efficiency are essential in UAV target recognition. Given the limited computational resources of UAVs and the system's high stability demands, existing complex models often do not meet practical application requirements. To tackle these challenges, this paper proposes LW-YOLOv8, a lightweight object detection algorithm based on the YOLOv8s model for UAV deployment. First, Cross Stage Partial Convolutional Neural Network (CNN) Transformer Fusion Net (CSP-CTFN) is proposed. It integrates convolutional neural networks and a multi-head self-attention (MHSA) mechanism, and achieves comprehensive global feature extraction through an expanded receptive field. Second, Parameter Shared Convolution Head (PSC-Head) is designed to enhance detection efficiency and further minimize model size. Furthermore, the original loss function is replaced with SIoU to enhance detection accuracy. Extensive experiments on the VisDrone2019 dataset show that the proposed model reduces parameters by 37.9%, computational cost by 22.8%, and model size by 36.9%, while improving AP, AP50, and AP75 by 0.2%, 0.2%, and 0.4%, respectively. The results indicate that the proposed model performs effectively in UAV recognition applications.