300 GHz radar object recognition based on deep neural networks and transfer learning

Sheeny, Marcel; Wallace, Andrew M.; Wang, Sen

doi:10.1049/iet-rsn.2019.0601

Cited by 21 publications

(10 citation statements)

References 130 publications

(282 reference statements)

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“…The Range-Doppler-Azimuth spectrums extracted from automotive radar sensors have been used frequently as 2D image inputs to various deep learning algorithms for different tasks, ranging from obstacle detection to segmentation, classification, and identification in autonomous driving systems [ 122 , 123 , 124 , 125 , 126 ]. The authors of [ 122 ] presented a method to recognize objects in Cartesian coordinates using a high-resolution 300-GHz scanning radar based on deep neural networks. They applied a fast Fourier transform (FFT) on each of the received signals to obtain the radar image.…”

Section: Detection and Classification Of Radar Signals Using Deep mentioning

confidence: 99%

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: Detection and Classification Of Radar Signals Using Deep mentioning

confidence: 99%

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

Abdu

Zhang

et al. 2021

Sensors

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“…Yang et al [40] used deep transfer learning to identify military targets under small training conditions and got excellent recognition effect results. Sheeny et al [41] identified objects in 300 GHz radar images based on depth neural network and migration learning and then considered detection and classification in multiple object scenes. The deep neural network is based on a large data set, and the weight is obtained after multiple training.…”

Section: Deep Transfermentioning

confidence: 99%

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

Wang

Yue

et al. 2021

International Journal of Aerospace Engineering

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Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.

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“…Frequency-Modulated Continuous-Wave (FMCW) radar is receiving increased attention for exploitation in autonomous applications, including for problems related to SLAM [6][7][8][9] as well as scene understanding tasks such as object detection [10], and segmentation [11]. This increasing popularity is evident in several urban autonomy datasets with a radar focus [12,13].…”

Section: Navigation and Scene Understanding From Radarmentioning

confidence: 99%

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

Broomé

Gadd

Martini

et al. 2020

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This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

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300 GHz radar object recognition based on deep neural networks and transfer learning

Abstract: The copyright in this thesis is owned by the author. Any quotation from the thesis or use of any of the information contained in it must acknowledge this thesis as the source of the quotation or information.

Cited by 21 publications

References 130 publications

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

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

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

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