Improved YOLOv8-Based Ground Small Object Perception for UAV

Abstract

To address the challenges of ground small-object detection in low-altitude UAV nadir-view scenes, such as small target scale, weak texture features, occlusion, and complex background interference, this paper proposes an improved YOLOv8-based method for UAV ground small-object detection. The network is lightweightly optimized by introducing CBAM and ECA into the backbone to enhance the representation of key regions and fine-grained features of small objects, adopting a PANet+ cross-scale feature fusion structure in the neck to improve multi-scale information interaction, and designing an improved decoupled detection head with an auxiliary objectness branch to improve localization accuracy, category discrimination, and small-object recall. Experiments on the VisDrone2019 dataset show that the proposed model achieves better detection performance and robustness in complex urban scenes. Compared with the baseline YOLOv8, Precision increases from 93.2% to 96.1%, mAP@0.5 improves from 55.2% to 64.5%, and Loss decreases from 0.48 to 0.08. The proposed method effectively improves the detection accuracy of UAV ground small objects while maintaining high real-time performance, providing support for UAV-based ground target perception and intelligent monitoring in complex scenes.