Efficient Real-Time Object Detection and Classification Using mmWave Radar and Jetson Xavier NX
Recently, mmWave radars have been used in several automotive and industrial applications to provide the position and speed of detected objects using FMCW algorithms. However, this radar is not able to recognize or classify the detected objects. This paper associates a mmWave radar with a real-time e...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11024013/ |
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| Summary: | Recently, mmWave radars have been used in several automotive and industrial applications to provide the position and speed of detected objects using FMCW algorithms. However, this radar is not able to recognize or classify the detected objects. This paper associates a mmWave radar with a real-time embedded computing platform and proposes a real-time implementation of deep learning algorithms in order to classify the detected objects. Three different classes of objects have been studied in this paper, i.e., human, car, and motorcycle. The proposed on-board system is based on the AWR2944EVM as a mmWave radar and the Nvidia Jetson Xavier NX as an embedded computing platform. The system was mounted on a motorcycle for data acquisition and real-time classification. The system developed captures radar data and processes it rapidly, converting it into usable formats for efficient object classification. Several YOLO detection deep learning models were evaluated, among which the YOLOv9-E model, which provided the best accuracy and speed performances for our application, i.e., a mAP_0.5 of 84% and an accuracy of 86.4%. The proposed architecture can process up to 18.59 frames per second (FPS). This system has been tested in a wide range of real-life conditions, validating its robustness and ability to operate in a variety of environments. The results obtained show that the proposed implementation delivers good real-time inference performance with optimized power consumption. |
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| ISSN: | 2169-3536 |