Vehicle Detection and Classification in Aerial Images using Convolutional Neural Networks

Due to the popularity of unmanned aerial vehicles, the acquisition of aerial images has become widely available. The aerial images have been used in many applications such as the investigation of roads, buildings, agriculture distribution, and land utilization, etc. In this paper, we propose a technique for vehicle detection and classification from aerial images based on the modification of Faster R-CNN framework. A new dataset for vehicle detection, VAID (Vehicle Aerial Imaging from Drone), is also introduced for public use. The images in the dataset are annotated with 7 common vehicle categories, including sedan, minibus, truck, pickup truck, bus, cement truck and trailer, for network training and testing. We compare the results of vehicle detection in aerial images with widely used network architectures and training datasets. The experiments demonstrate that the proposed method and dataset can achieve high vehicle detection and classification rates under various road and traffic conditions.