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.

翻译：在现代驾驶计算环境中,许多传感器被用于环境认知应用。本文使用由进化神经网络组成的U-Net和高效网络两种深层学习模型,以探测手势,并清除通过毫米波雷达测量的多普勒地图图像中的噪音。为了改进分类的性能,准确的预处理算法至关重要。因此,在进入第一个深层学习模型阶段之前对隐蔽图像采用新的预处理方法提高了分类的准确性。因此,本文建议采用基于深神经网络的高性能非线性预处理方法。