LWU-YOLO: A lightweight algorithm for small object detection in UAV applications

Abstract

Since detecting small objects in UAV imagery is challenging due to complex backgrounds and limited pixels, this paper proposes a new lightweight model based on YOLOv8s called LWU-YOLO. Initially, a task-oriented head restructuring strategy is introduced to enhance detailed feature representation, while reducing model parameters. Subsequently, an efficient multi-scale downsampling feature fusion (MDFF) module is designed to minimize the information loss during the upsampling process. Moreover, a mixed local channel attention (MLCA) mechanism is integrated into the C2f module to improve focus on critical features. Additionally, a novel Inner-PIoUv2 loss function is devised for faster convergence and higher accuracy in small object regression. Finally, experiments on the VisDrone2019 dataset show that the LWU-YOLO increases mAP@50 and mAP@50:95 by 7.3% and 4.7%, respectively, while using 55.3% fewer parameters than YOLOv8s, demonstrating an excellent balance of performance and efficiency for UAV applications.