2D Car Detection in Radar Data with PointNets

Danzer, Andreas; Griebel, Thomas; Bach, Martin; Dietmayer, Klaus

doi:10.1109/itsc.2019.8917000

Cited by 138 publications

(77 citation statements)

References 11 publications

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“…Furthermore, weight sharing and invariance to translation, scaling, rotation, and other transformations of the input data are essential in recognizing radar signals. Over the past few years, DCNNs have been employed to process various types of millimeter-wave radar data for object detection, recognition, human activity classification, and many more tasks [ 37 , 38 , 39 , 45 , 47 ], with excellent performance accuracy and efficiency. This is due to their ability to extract high-level abstracted features by exploiting the radar signal’s structural locality.…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

“…Some studies have recently started implementing deep learning models using radar point clouds for different applications [ 45 , 49 , 50 , 51 , 143 , 144 ]. The authors of [ 51 ] presented the first article that employed radar point clouds for semantic segmentation.…”

Section: Detection and Classification Of Radar Signals Using Deepmentioning

confidence: 99%

“…In essence, they assigned a class label to each of the measured radar reflections. In reference [ 45 ], Andreas Danzer et al employed the PointNet ++ [ 145 ] model using radar point clouds for 2D object classifications and bounding box estimations. They used the popularly known PointNets family model, which was ideally designed to consume 3D Lidar point clouds, and adjusted it to fit radar point clouds with different attributes and characteristics.…”

Section: Detection and Classification Of Radar Signals Using Deepmentioning

confidence: 99%

“…Over the recent years, some radar signal datasets are being reported for public usage [ 41 , 42 , 43 , 44 ]. As a result, many researchers have begun to apply radar signals as inputs to various deep learning networks for object detection [ 45 , 46 , 47 , 48 ], object segmentation [ 49 , 50 , 51 ], object classification [ 52 ], and their combination with vision data for deep-learning-based multi-modal object detection [ 53 , 54 , 55 , 56 , 57 , 58 ]. This paper specifically reviewed the recent articles on deep learning-based radar data processing for object detection and classification.…”

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

Sensors

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The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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Section: Overview Of Deep Learningmentioning

confidence: 99%

Section: Detection and Classification Of Radar Signals Using Deepmentioning

confidence: 99%

Section: Detection and Classification Of Radar Signals Using Deepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

Sensors

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“…However, the output of a single radar sweep is too sparse. To overcome this, they used multiple frames [11] or multiple radar sensors [20].…”

Section: Related Workmentioning

confidence: 99%

CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

141

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This paper presents a novel radar based, singleframe, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar targetand object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets' positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target-and objectwise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.

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Introduction to Radar Processing and Deep Learning

2022

Methods and Techniques in Deep Learning

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2D Car Detection in Radar Data with PointNets

Cited by 138 publications

References 11 publications

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

CNN Based Road User Detection Using the 3D Radar Cube

Introduction to Radar Processing and Deep Learning

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