RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
Ramin Nabati,Hairong Qi +1 more
- pp 3093-3097
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TLDR
RRPN is introduced, a Radar-based real-time region proposal algorithm for object detection in autonomous driving vehicles that operates more than 100× faster while at the same time achieves higher detection precision and recall.Abstract:
Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in the image. Nonetheless, region proposal algorithms are known to be the bottleneck in most two-stage object detection networks, increasing the processing time for each image and resulting in slow networks not suitable for real-time applications such as autonomous driving vehicles. In this paper we introduce RRPN, a Radar-based real-time region proposal algorithm for object detection in autonomous driving vehicles. RRPN generates object proposals by mapping Radar detections to the image coordinate system and generating pre-defined anchor boxes for each mapped Radar detection point. These anchor boxes are then transformed and scaled based on the object’s distance from the vehicle, to provide more accurate proposals for the detected objects. We evaluate our method on the newly released NuScenes dataset [1] using the Fast R-CNN object detection network [2]. Compared to the Selective Search object proposal algorithm [3], our model operates more than 100× faster while at the same time achieves higher detection precision and recall. Code has been made publicly available at https://github.com/mrnabati/RRPN.read more
Citations
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Proceedings ArticleDOI
YOLO v3-Tiny: Object Detection and Recognition using one stage improved model
TL;DR: This paper presents the fundamental overview of object detection methods by including two classes of object detectors, including YOLO v1, v2, v3, and SSD, and its comparison with previous methods for detection and recognition of object is described graphically.
Proceedings ArticleDOI
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
Ramin Nabati,Hairong Qi +1 more
TL;DR: CenterFusion as discussed by the authors uses a center point detection network to detect objects by identifying their center points on the image and then solves the key data association problem using a novel frustum-based method to associate the radar detections to their corresponding object's center point.
Proceedings ArticleDOI
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
Ramin Nabati,Hairong Qi +1 more
TL;DR: This paper proposes a middle-fusion approach to exploit both radar and camera data for 3D object detection and solves the key data association problem using a novel frustum-based method.
Proceedings ArticleDOI
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals
TL;DR: In this article, a two-stage deep fusion detector is proposed, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results.
Journal ArticleDOI
Automotive Radar Signal Processing: Research Directions and Practical Challenges
Florian Engels,Philipp Heidenreich,Markus Wintermantel,Lukas Stäcker,Muhammed Al Kadi,Abdelhak M. Zoubir +5 more
TL;DR: A comprehensive signal model for the multiple-target case using multiple-input multiple-output schemes, and a practical processing chain to calculate the target list is provided.
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