Abstract
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.
Highlights
The automotive market has been in the spotlight in various fields and has been growing rapidly
This study showed the feasibility of object detection and 3D target estimation with fully connected neural network (FCN)
This paper proposes a simultaneous target detection and classification model that combines an automotive radar system with the you only look once (YOLO) network
Summary
The automotive market has been in the spotlight in various fields and has been growing rapidly. This paper proposes a simultaneous target detection and classification model that combines an automotive radar system with the YOLO network. The target detection results from the range-angle (RA) domain are obtained through radar signal processing, and YOLO is trained using the transformed RA domain data. We compare the detection and classification performance of our proposed method with those of the conventional methods used in radar signal processing. The classification performance of YOLO by training it with the radar data measured in the RD domain was proposed in [22]. Both the above-mentioned methods [21,22] dealt with radar data in the RD domain.
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