Robust Vehicle Detection on Multi- Resolution Aerial Images

Detecting vehicles in aerial images is one of the core components for intelligent transportation system. This task is challenging due to the comparably small size of the target objects, the complex background and multi-perspective views. It is particularly difficult for the real-time detection cases where only several tens of milliseconds delays are allowed. In this paper, we propose an approach to detect vehicles from aerial images with different resolutions and perspectives in an approximate real-time manner. The proposed model is robust and can detect vehicles in various detection scenarios. It can detect vehicles from an image within 47 milliseconds. We evaluate our method on a challenging data set of original aerial images over Munich and our data set collected using an unmanned aerial vehicle (UAV). The experimental results have demonstrated that our proposed method is superior to the state-of-the-art algorithms with respect to accuracy, recall and detection time.