The availability of commercial UAVs and low-cost imaging devices has made the airborne imagery popular and widely available. The aerial images are now extensively used for many applications, especially in the area of intelligent transportation systems. In this work, we present a new aerial image dataset, VAID (Vehicle Aerial Imaging from Drone), for the development and evaluation of vehicle detection algorithms. It contains about 6,000 images captured under different traffic conditions, and annotated with 7 common vehicle categories for network training and testing. We compare the of vehicle detection results using the current state-of-the-art network architectures and various aerial image datasets. The experiments have demonstrated that training the networks using our VAID dataset can provide the best vehicle detection results.

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