Target Classification Using Combined YOLO-SVM in High-Resolution Automotive FMCW Radar

In this paper, we propose a method to classify the human and vehicle objects by combining a support vector machine (SVM) and the deep learning model, you only look once (YOLO), in a high-resolution automotive radar system. To enhance the classification performance, the boundaries of targets estimated from the YOLO model are delivered to the SVM. Then, the overall classification accuracy can be improved by combining the results from the YOLO and SVM with predefined target boundaries. The results show that the proposed method has a better classification performance than those obtained using only YOLO or SVM. From the actual measurements, the proposed method can classify humans and vehicles over 90% accuracy in high-resolution automotive radar.