RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization

• URL: http://arxiv.org/abs/2102.05150v1
• Date: Tue, 9 Feb 2021 22:01:55 GMT
• Title: RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization
• Authors: Yizhou Wang, Zhongyu Jiang, Yudong Li, Jenq-Neng Hwang, Guanbin Xing, Hui Liu
• Abstract summary: We propose a deep radar object detection network, named RODNet, which is cross-supervised by a camera-radar fused algorithm. Our proposed RODNet takes a sequence of RF images as the input to predict the likelihood of objects in the radar field of view (FoV) With intensive experiments, our proposed cross-supervised RODNet achieves 86% average precision and 88% average recall of object detection performance.
• Score: 30.42848269877982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used sensors, radar has usually been considered as a robust and cost-effective solution even in adverse driving scenarios, e.g., weak/strong lighting or bad weather. Instead of considering to fuse the unreliable information from all available sensors, perception from pure radar data becomes a valuable alternative that is worth exploring. In this paper, we propose a deep radar object detection network, named RODNet, which is cross-supervised by a camera-radar fused algorithm without laborious annotation efforts, to effectively detect objects from the radio frequency (RF) images in real-time. First, the raw signals captured by millimeter-wave radars are transformed to RF images in range-azimuth coordinates. Second, our proposed RODNet takes a sequence of RF images as the input to predict the likelihood of objects in the radar field of view (FoV). Two customized modules are also added to handle multi-chirp information and object relative motion. Instead of using human-labeled ground truth for training, the proposed RODNet is cross-supervised by a novel 3D localization of detected objects using a camera-radar fusion (CRF) strategy in the training stage. Finally, we propose a method to evaluate the object detection performance of the RODNet. Due to no existing public dataset available for our task, we create a new dataset, named CRUW, which contains synchronized RGB and RF image sequences in various driving scenarios. With intensive experiments, our proposed cross-supervised RODNet achieves 86% average precision and 88% average recall of object detection performance, which shows the robustness to noisy scenarios in various driving conditions.

Related papers

• A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data [7.2508100569856975]
  We use the raw range-Doppler spectrum of radar data to process camera images. We extract the corresponding features with our camera encoder-decoder architecture. The resultant feature maps are fused with Range-Azimuth features, recovered from the RD spectrum input to perform object detection.
  arXiv  Detail & Related papers  (2024-11-20T13:26:13Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion [14.419658061805507]
  We propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios. The framework benefits from dense contextual information from both the range-doppler spectrum and images which are integrated to learn a multi-modal feature representation.
  arXiv  Detail & Related papers  (2023-07-17T04:25:46Z)
• Multi-Modal 3D Object Detection by Box Matching [109.43430123791684]
  We propose a novel Fusion network by Box Matching (FBMNet) for multi-modal 3D detection. With the learned assignments between 3D and 2D object proposals, the fusion for detection can be effectively performed by combing their ROI features.
  arXiv  Detail & Related papers  (2023-05-12T18:08:51Z)
• CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection [12.557361522985898]
  We propose a camera-radar matching network CramNet to fuse the sensor readings from camera and radar in a joint 3D space. Our method supports training with sensor modality dropout, which leads to robust 3D object detection, even when a camera or radar sensor suddenly malfunctions on a vehicle.
  arXiv  Detail & Related papers  (2022-10-17T17:18:47Z)
• NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [57.03126447713602]
  We present a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
  arXiv  Detail & Related papers  (2022-09-29T01:30:34Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment [38.24705460170415]
  We propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system. CRUW aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. To the best of our knowledge, CRUW is the first public large-scale dataset with a systematic annotation and evaluation system.
  arXiv  Detail & Related papers  (2021-05-11T17:13:45Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)
• RODNet: Radar Object Detection Using Cross-Modal Supervision [34.33920572597379]
  Radar is usually more robust than the camera in severe driving scenarios. Unlike RGB images captured by a camera, semantic information from the radar signals is noticeably difficult to extract. We propose a deep radar object detection network (RODNet) to effectively detect objects purely from the radar frequency data.
  arXiv  Detail & Related papers  (2020-03-03T22:33:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.