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Open AccessArticle

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

Machine Learning Lab, AI & SW Research Center, Samsung Advanced Institute of Technology (SAIT), 130, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Korea
Department of Radio and Information Communications Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
School of Electronics and Information Engineering, Korea Aerospace University, 76, Deogyang-gu, Goyang-si, Gyeonggi-do 10540, Korea
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2897;
Received: 24 April 2020 / Revised: 14 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Section Electronic Sensors)
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. View Full-Text
Keywords: automotive FMCW radar; target classification; object detection; YOLO automotive FMCW radar; target classification; object detection; YOLO
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Kim, W.; Cho, H.; Kim, J.; Kim, B.; Lee, S. YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems. Sensors 2020, 20, 2897.

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