YOLODrone: improved YOLO architecture for object detection in drone images

Abstract

Recent advances in robotics and computer vision fields yield emerging new applications for camera equipped drones. One such application is aerial-based object detection. However, despite the recent advances in the relevant literature, object detection remains as a challenging task in computer vision. Existing object detection algorithms demonstrate even lower performance on drone (or aerial) images since the object detection problem is a more challenging problem in aerial images, when compared to the detection task in ground-taken images. There are many reasons for that including: (i) the lack of large drone datasets with large object variance, (ii) the larger variance in both scale and orientation in drone images, and (iii) the difference in shape and texture features between the ground and the aerial images. In this paper, we introduce an improved YOLO algorithm: YOLODrone for detecting objects in drone images. We evaluate our algorithm on VisDrone2019 dataset and report improved results when compared to YOLOv3 algorithm.