Small object detection and tracking from aerial imagery

Abstract

Object detection and tracking from airborne imagery draws attention to the parallel development of UAV systems and computer vision technologies. Aerial imagery has its own unique challenges that differ from the training set of modern-day object detectors, since it is made of images of larger areas compared to the regular datasets and the objects are very small on the contrary. These problems do not allow us to use common object detection models. The main purpose of this paper is to make modifications to the Faster-RCNN (FRCNN) model, then leverage it for small object detection and tracking from the aerial imagery. It is aimed to use both spatial and temporal information from the image sequence, as appearance information alone is insufficient. The anchors in the Region Proposal Network (RPN) stage will be adjusted for small objects. Also, intersection over union (IoU) is optimized for small objects. After improving detection performance, The DeepSORT algorithm is inserted right after the Region of Interest (ROI Head) to track the objects. The results show that the proposed model has good performance on the VisDrone-2019 dataset. Detection performance becomes considerably better than the original FRCNN and the algorithms that are evaluated in the VisDrone-2019 VID challenge. After completing the proposed modifications, the AP-AP50 values reached 14.07-29.41 from 8.08-18.70, which means approximately 75% improvement.