Fast multidirectional vehicle detection on aerial images using region based convolutional neural networks
Tianyu Tang, Shilin Zhou, Zhipeng Deng, Lin Lei, Huanxin Zou · 2017
This paper proposes a coupled region based convolutional neural networks (R-CNN) to automatically detect vehicles in aerial images. Traditional methods are mostly based on sliding-window search, and use handcrafted or shallow-learning based features. They have limited description ability and heavy computational costs. Recently, a series of R-CNN based methods have achieved great success in general object detection. Inspired by the previous work, we propose a coupled R-CNN to detect small size vehicles in large-scale aerial images. First, a vehicle proposal network (VPN) is proposed to generate candidate vehicle-like regions, using a hyper feature map combined by feature maps of different layers. Then, a vehicle classification network (VCN) is developed to further verify the candidate regions and classify vehicles in eight directions. In this study, our method is tested on a challenge Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and speed compared to existing methods.