YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems
Journal
sensors
Date
2020.05.20
Abstract
This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, especially you only look once (YOLO), with pre-processed automotive radar signals. In the conventional method, 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 apply 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.