RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
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Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in each image. Nonetheless, region proposal generators are known to be the bottleneck in these two-stage object detection networks, making them slow and not suitable for real-time applications such as autonomous vehicles. In this paper we introduce a Radar-based real-time region proposal algorithm for object detection in autonomous vehicles. The proposed Regions of Interest (RoI) are generated by mapping Radar detections to the image coordinate system and generating pre-defined anchor boxes as object proposals at each mapped Radar point. We then perform transformation and scaling operations on the generated anchors based on objects' distance to provide better fit for the detected objects. We evaluate our method on the newly released NuScenes dataset using the Fast R-CNN object detection network. Compared to the Selective Search object proposal algorithm, our model operates more than 100x faster while at the same time achieves higher detection precision and recall. Code has been made publicly available at https://github.com/mrnabati/RRPN.
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