YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Woosuk Kim; Hyunwoong Cho; Jongseok Kim; Byungkwan Kim; Seongwook Lee

doi:10.3390/s20102897

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Sensors ◽

10.3390/s20102897 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2897 ◽

Cited By ~ 2

Author(s):

Woosuk Kim ◽

Hyunwoong Cho ◽

Jongseok Kim ◽

Byungkwan Kim ◽

Seongwook Lee

Keyword(s):

Spatial Clustering ◽

Radar Data ◽

Support Vector ◽

Automotive Radar ◽

Radar Sensor ◽

Fmcw Radar ◽

Radar Systems ◽

Conventional Methods ◽

Two Stages ◽

Deep Learning Model

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

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Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Sensors ◽

10.3390/s20072001 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2001 ◽

Cited By ~ 2

Author(s):

Eugin Hyun ◽

YoungSeok Jin

Keyword(s):

Classification Scheme ◽

Vehicle Classification ◽

Doppler Spectrum ◽

Support Vector ◽

Radar Sensor ◽

Moving Vehicle ◽

Fmcw Radar ◽

Wave Radar ◽

Spectrum Feature ◽

Feature Based

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

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Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

Electronics ◽

10.3390/electronics9040573 ◽

2020 ◽

Vol 9 (4) ◽

pp. 573 ◽

Cited By ~ 1

Author(s):

Onur Toker ◽

Suleiman Alsweiss

Keyword(s):

Autonomous Vehicles ◽

Electromagnetic Waves ◽

Continuous Wave ◽

Velocity Estimation ◽

Automotive Radar ◽

Radar Sensor ◽

Detection Scheme ◽

Fmcw Radar ◽

Core Idea ◽

77 Ghz

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.

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Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

Sensors ◽

10.3390/s20123504 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3504 ◽

Cited By ~ 1

Author(s):

Qisong Wu ◽

Teng Gao ◽

Zhichao Lai ◽

Dianze Li

Keyword(s):

Error Function ◽

Class Imbalance ◽

Radar Data ◽

Classification Performance ◽

Vehicle Classification ◽

Support Vector ◽

Automotive Radar ◽

Deep Network ◽

Classification Technique ◽

Doppler Image

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.

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Automotive Radars

10.34048/2017.2.f3 ◽

2017 ◽

Author(s):

Sujeet Patole ◽

Murat Torlak ◽

Dan Wang ◽

Murtaza Ali

Keyword(s):

Signal Processing ◽

Pedestrian Detection ◽

Radar Signal ◽

Automotive Radar ◽

Estimation Algorithms ◽

Radar Systems ◽

Starting Point ◽

Processing Techniques ◽

Automotive Radars ◽

Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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Mutual Interference Suppression Using Wavelet Denoising in Automotive FMCW Radar Systems

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2019.2961235 ◽

2020 ◽

pp. 1-11

Author(s):

Seongwook Lee ◽

Jung-Yong Lee ◽

Seong-Cheol Kim

Keyword(s):

Interference Suppression ◽

Wavelet Denoising ◽

Mutual Interference ◽

Fmcw Radar ◽

Radar Systems

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Comparison Between Conventional Methods, Neural Network, and Support Vector Regression in Forecasting Foreign Tourists in Indonesia

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1125/1/012056 ◽

2021 ◽

Vol 1125 (1) ◽

pp. 012056

Author(s):

Purwati ◽

Donny Montreano ◽

Alina Cynthia Dewi

Keyword(s):

Neural Network ◽

Support Vector Regression ◽

Support Vector ◽

Conventional Methods

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PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

BMC Bioinformatics ◽

10.1186/s12859-020-03870-2 ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Jun Meng ◽

Qiang Kang ◽

Zheng Chang ◽

Yushi Luan

Keyword(s):

Deep Learning ◽

Noncoding Rna ◽

Nearest Neighbor ◽

Short Term Memory ◽

Biological Activities ◽

Support Vector ◽

Multiple Perspectives ◽

K Nearest Neighbor ◽

Rna Sequences ◽

Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

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