Camera-equipped drones can capture targets on the ground from a wider field of view than static cameras or moving sensors over the ground. In this project, we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform. The images in the benchmark were captured over various urban areas, which include different types of urban roads, residential areas, parking lots, highways, etc., from day to night. Specifically, DroneVehicle consists of 15,532 pairs of images, i.e., RGB images and infrared images with rich annotations, including oriented object bounding boxes, object categories, etc. With intensive amount of effort, our benchmark has 441,642 annotated instances in 31,064 images.

The challenge mainly focuses on two tasks:

Task 1: object detection in images. Given a predefined set of object classes (e:g:, car, bus, and truck), the task aims to detect objects of these classes from individual images taken from drones.

Task 2: object counting in images. The task aims to estimate the number of vehicles from individual images in DroneVehicle

@misc{zhu2020drone, title={Drone Based RGBT Vehicle Detection and Counting: A Challenge}, author={Pengfei Zhu and Yiming Sun and Longyin Wen and Yu Feng and Qinghua Hu}, year={2020}, eprint={2003.02437}, archivePrefix={arXiv}, primaryClass={cs.CV} }