LGFF-YOLO: small object detection method of UAV images based on efficient local–global feature fusion

Abstract

Images captured by Unmanned Aerial Vehicles (UAVs) play a significant role in many fields. However, with the development of UAV technology, challenges such as detecting small and dense objects against complex backgrounds have emerged. In this paper, we propose LGFF-YOLO, a detection model that integrates a novel local–global feature fusion method with the YOLOv8 baseline, specifically designed for small object detection in UAV imagery. Our innovative approach employs the Global Information Fusion Module (GIFM) and the Four-Leaf Clover Fusion Module (FLCM) to enhance the fusion of multi-scale features, improving detection accuracy without increasing model complexity. Next, we proposed the RFA-Block and LDyHead to control the total number of model parameters and improve the representation capability for small object detection. Experimental results on the VisDrone2019 dataset demonstrate a 38.3% mAP with only 4.15M parameters, a 4. 5% increase over baseline YOLOv8, while achieving 79.1 FPS for real-time detection. These advancements enhance the model’s generalization capability, balancing accuracy and speed, and significantly extend its applicability for detecting small objects in UAV images.