YOLO-EMR: Efficient Multi-Scale and Rotated Object Detection in UAV Aerial Imagery

With the rapid development of UAVs, object detection in aerial imagery has become an important techniques. However, deploying real-time detection models on UAV platforms remains highly challenging due to limited computational costs, as well as the multi-scale objects and dense distribution caused by high-altitude imaging. Numerous studies have contributed valuable insights to address these challenges, yet opportunities remain for improving the balance between model efficiency and detection accuracy. Moreover, current research mainly focuses on small object detection, without fully considering the detection requirements for multi-scale and rotated objects in high-altitude imagery. To address these issues, this paper proposes a lightweight object detection model specifically designed for multi-scale and rotated object detection in high-altitude imagery. Furthermore, to better evaluate the model’s performance in UAV-based object detection in high-altitude imagery, this work uses VisDrone 2019 dataset to assess the model’s real-world performance. As a results, compared to existing object detection approaches, the proposed model achieves a strong balance between detection accuracy, model efficiency, and inference speed, reducing parameters by approximately 31% and improving inference speed by 25%, while maintaining the same detection accuracy as the best baseline methods.