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Region proposal algorithms play an important role in most state-of-the-arttwo-stage object detection networks by hypothesizing object locations in theimage. Nonetheless, region proposal algorithms are known to be the bottleneckin most two-stage object detection networks, increasing the processing time foreach image and resulting in slow networks not suitable for real-timeapplications such as autonomous driving vehicles. In this paper we introduceRRPN, a Radar-based real-time region proposal algorithm for object detection inautonomous driving vehicles. RRPN generates object proposals by mapping Radardetections to the image coordinate system and generating pre-defined anchorboxes for each mapped Radar detection point. These anchor boxes are thentransformed and scaled based on the object's distance from the vehicle, toprovide more accurate proposals for the detected objects. We evaluate ourmethod on the newly released NuScenes dataset [1] using the Fast R-CNN objectdetection network [2]. Compared to the Selective Search object proposalalgorithm [3], our model operates more than 100x faster while at the same timeachieves higher detection precision and recall. Code has been made publiclyavailable at https://github.com/mrnabati/RRPN .

RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles