论文标题
基于MMWave雷达的人类手势的神经架构非线性预处理
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
论文作者
论文摘要
在现代的驾驶计算环境中，许多传感器用于上下文感知的应用程序。本文利用了由卷积神经网络（CNN）组成的两个深度学习模型，它们可以检测手势，并在多普勒映射范围内通过毫米波（mmwave）雷达进行测量的噪声。为了提高分类的性能，必不可少的准确处理算法。因此，在进入第一个深度学习模型阶段之前，一种新型的预处理方法可以提高分类的准确性。因此，本文提出了一种基于神经网络的深度高性能非线性预处理方法。
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.
