Scale-Aware RPN for Vehicle Detection

In this paper, we develop a scale-aware Region Proposal Network (RPN) model to address the problem of vehicle detection in challenging situations. Our model introduces two built in sub-networks which detect vehicles with scales from disjoint ranges. Therefore, the model is capable of training the specialized sub-networks for large-scale and small-scale vehicles in order to capture their unique characteristics. Meanwhile, high resolution of feature maps for handling small vehicle instances is obtained. The network model is followed by two XGBoost classifiers with bootstrapping strategy for mining hard negative examples. The method is evaluated on the challenging KITTI dataset and achieves comparable results against the state-of-the-art methods.