An improved YOLOv5-based method for UAV object detection

Object detection based on unmanned aerial vehicle (UAV) images is very challenging. The multi-scale size and high density of objects in the UAV view bring great difficulties. To fully address this issue to unleash the potential of UAV applications, the YOLOv5-STD model is proposed. First, we add one more head to locate extremely small object detection by shallow image features; second, we use the attention mechanism to optimize the backbone by the transformer; third, we use SPD-Conv to avoid the loss of fine-grained image feature information. At the last, sufficient experiments on the dataset VisDrone 2022 have proven that the model has good performance, compared with the basic model, the improved model has an average improvement of about 7% in mAP@.5 metrics, and the ablation experiments have verified that its improvement skills have a positive effect on the model. This paper can help developers and researchers get a better experience in the analysis and processing of unmanned aerial vehicle images.