Enhanced YOLOv8 Framework for Precise Small Object Detection in UAV Imagery

Abstract

The rapid advancement of UAV technology is accelerating the intelligent transformation of object detection methods. UAV applications now span both civilian and military sectors, playing crucial roles in scenarios such as aerial surveying, disaster response, and security monitoring. However, UAV image detection faces numerous challenges: targets are affected by flight altitude and viewing angle, leading to significant scale variations; dynamic flight results in constantly changing spatial layouts and complex backgrounds—such as dense vegetation and building textures—often weaken or obscure object features, making detection more difficult. To tackle these issues, we present a novel YOLOv8-based model tailored for small object detection in UAV images. In response to the prevalence of small targets in UAV images, the model enhances the multi-scale network structure and introduces the Large Separable Kernel Attention-enhanced Spatial Pyramid Pooling Fast Layer (SPPF-LSKA), improving the model’s capability to extract features from and respond to small objects. Furthermore, the introduction of the Minimum Point Distance IoU (MPDIoU) and Soft Non-Maximum Suppression (Soft-NMS) enables effective detection of overlapping and occluded targets. Experimental results on the VisDrone2019 dataset show that, compared with YOLOv8, the improved model increases mAP@0.5 by 7.6%, reduces parameter count by 23%, and has a model size of just 5MB. Compared with other current algorithms, the enhanced model delivers the best detection performance, highlighting the effectiveness of the proposed approach.