Improved YOLOv4 for aerial object detection

Abstract

Drones equipped with cameras are being used for surveillance purposes. These surveillance systems need vision-based object detection of ground objects which look very small because of the altitude of drones. We propose an improved YOLOv4 model targeted for vision-based small object detection. We investigated the performance of state of the art YOLOv4 object detector on the VisDrone dataset. We enhanced the features of small objects by connecting Upsampling layers and concatenating the upsampled features with the original features to obtain more refined and grained features for small objects. Experiments showed that the modified YOLOv4 achieved 2 percent better mAP results as compared to the original YOLOv4 at different image resolutions on the VisDrone dataset while running at the same speed as the original YOLOv4.