Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals

Capobianco, Samuele; Facheris, Luca; Cuccoli, Fabrizio; Marinai, Simone

doi:10.1007/978-3-319-75608-0_9

Cited by 36 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of DL methods for target classification in a 2D space, instead, have been studied in refs. [15], [124]- [131]. It is worth mentioning that, in the technical literature, the first results about the use of DL methods in automotive radar systems appeared after 2015, when it was found that DCNNs were able to simultaneously detect, localize and classify multiple targets by simply analysing 2D range-azimuth (or range-Doppler) maps.…”

Section: Autonomous Drivingmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

View full text Add to dashboard Cite

<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

show abstract

Section: Autonomous Drivingmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

DAVOLI¹,

Guerzoni²,

Vitetta³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides, the lack of open-access datasets and benchmarks containing radar signals have contributed to the fewer research outputs over the years [ 18 ]. As a result, many researchers self-developed their own radar signal datasets to test their proposed algorithms for object detection and classification using different radar data representations as inputs to the neural networks [ 36 , 37 , 38 , 39 , 40 ]. However, as these datasets are inaccessible, comparisons and evaluations are not possible.…”

Section: Introductionmentioning

confidence: 99%

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

Abdu

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Research on exploiting DNNs for analyzing radar signals is still at an early stage. [6] considered the problem of classifying 6 different vehicles using the frequency-modulated continuous-wave (FMCW) radar signals, where Short Time Fourier Transformation (STFT) is firstly applied to the original radar signals to obtain spectrums as inputs to the DNN. In [7], the authors attempted to detect the presence of vehicles using DNNs, which can be formulated as a binary classification problem.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.

show abstract

Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals

Cited by 36 publications

References 11 publications

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

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

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Contact Info

Product

Resources

About