Abstract
The vehicle detection in the aerial images is widely used in many applications. Comparing with the object detection in the ground view images, vehicle detection in the aerial images remains a challenging problem due to the small size of vehicles, monotone appearance and complex background. In this paper, we propose the solution of this issue using the convolutional neural networks. We further introduce the large-scale vehicle detection dataset with ground truth annotations for all the vehicles in the scene that considers the scene complexity due to the environmental conditions. We show the performance of the trained model with other popular neural work architectures.
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