GLDS-YOLO: An Improved Lightweight Model for Small Object Detection in UAV Aerial Imagery
Zhiyong Ju, Jiacheng Shui, Jiameng Huang · Electronics · 2025
To enhance small object detection in UAV aerial imagery suffering from low resolution and complex backgrounds, this paper proposes GLDS-YOLO, an improved lightweight detection model. The model integrates four core modules: Group Shuffle Attention (GSA) to strengthen small-scale feature perception, Large Separable Kernel Attention (LSKA) to capture global semantic context, DCNv4 to enhance feature adaptability with reduced parameters, and further proposes a novel Small-object-enhanced Multi-scale and Structure Detail Enhancement (SMSDE) module, which enhances edge-detail representation of small objects while maintaining lightweight efficiency. Experiments on VisDrone2019 and DOTA1.0 demonstrate that GLDS-YOLO achieves superior detection performance. On VisDrone2019, it improves [email protected] and [email protected]:0.95 by 12.1% and 7%, respectively, compared with YOLOv11n, while maintaining competitive results on DOTA. These results confirm the model’s effectiveness, robustness, and adaptability for complex small object detection tasks in UAV scenarios.