YOLO-MARS: An Enhanced YOLOv8n for Small Object Detection in UAV Aerial Imagery

In unmanned aerial vehicle (UAV) aerial imagery scenarios, challenges such as small target size, compact distribution, and mutual occlusion often result in missed detections and false alarms. To address these challenges, this paper introduces YOLO-MARS, a small target recognition model that incorporates a multi-level attention residual mechanism. Firstly, an ERAC module is designed to enhance the ability to capture small targets by expanding the feature perception range, incorporating channel attention weight allocation strategies to strengthen the extraction capability for small targets and introducing a residual connection mechanism to improve gradient propagation stability. Secondly, a PD-ASPP structure is proposed, utilizing parallel paths for differentiated feature extraction and incorporating depthwise separable convolutions to reduce computational redundancy, thereby enabling the effective identification of targets at various scales under complex backgrounds. Thirdly, a multi-scale SGCS-FPN fusion architecture is proposed, adding a shallow feature guidance branch to establish cross-level semantic associations, thereby effectively addressing the issue of small target loss in deep networks. Finally, a dynamic WIoU evaluation function is implemented, constructing adaptive penalty terms based on the spatial distribution characteristics of predicted and ground-truth bounding boxes, thereby optimizing the boundary localization accuracy of densely packed small targets from the UAV viewpoint. Experiments conducted on the VisDrone2019 dataset demonstrate that the YOLO-MARS method achieves 40.9% and 23.4% in the mAP50 and mAP50:95 metrics, respectively, representing improvements of 8.1% and 4.3% in detection accuracy compared to the benchmark model YOLOv8n, thus demonstrating its advantages in UAV aerial target detection.