YOLO-A2G: An air-to-ground high-precision object detection algorithm based on YOLOv5

Air-to-ground object detection is playing an increasingly important role in a variety of ground awareness and cognitive missions such as fighter aircraft attempting to assault and defend ground defense barrier fortifications and strike and destroy ground objects. However, air-to-ground object detection becomes very challenging due to the insufficient number of battlefield samples in air-to-ground imaging, many ground background disturbances and large-scale variation. In this paper, an improved air-to-ground object detection algorithm, YOLO-A2G, is proposed to solve this problem based on YOLOv5. In YOLO-A2G, firstly, in response to the insufficient number of samples, we used the direct and inverse Visual Focus (VF) affine a data augmentation algorithm to enrich and expand the samples in addition to the original data augmentation algorithm of YOLOv5. We then introduced the Coordinate Attention (CA) mechanism into the head network of YOLOv5 to autonomously learn explicit and implicit knowledge for the purpose of feature focusing and redundancy removal. Finally, in the post-processing stage after the network prediction, we used Weighted Boxes Fusion (WBF) instead of the traditional NMS to achieve spatial scale fusion. We performed an experimental validation using the Air-to-Ground (A2G) dataset and mAP of YOLO-A2G reached 94%.