SUA-YOLO: A Novel YOLO-Based Algorithm for Detecting Dense Small Objects From a Low-Altitude UAV Perspective

Unmanned aerial vehicle (UAV) aerial photography technology is crucial in various fields, including daily life, production, and security. However, it faces challenges in small object detection, such as extremely small objects, large scale variations, dense occlusions, and complex backgrounds. This paper presents SUA-YOLO, an improved solution based on the YOLOv8 model, to tackle these issues. First, a fine-grained detection layer is added to reconstruct the model framework, addressing the feature compression problem in deep networks. Second, the improved C2f_SCSAR module embeds Spatial and Channel Synergistic Attention (SCSA) to suppress background noise and enhance critical feature responses, thereby improving the model’s feature extraction capability in complex backgrounds. Next, the RepNCSPELAN4 module employs a multi-branch architecture to enhance feature representation, thereby improving cross-scale feature fusion of the Neck. Additionally, the Universal Adaptive Feature Fusion (UAFF) mechanism is constructed to balance detail and semantic features, further enhancing feature fusion capabilities and improving the model’s robustness across various scenarios. Finally, the Inner-WIoU loss function is integrated to improve the model’s adaptability in dense occlusion situations, effectively addressing false negatives. Using YOLOv8n as the baseline, we conducted object detection experiments on the VisDrone2019 dataset. The improved model achieved increases of 7.1% in mAP@0.5 and 4.8% in mAP@0.5:0.95. Additionally, generalization experiments on the DOTA dataset showed that SUA-YOLO performs effectively in small object detection tasks across various scenarios.