In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.
This research was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant through the Korean Government [Ministry of Science and ICT (MSIT)] under Grant 2021-0-00467. J.Kim is a corresponding author of this paper.
