Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
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.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Concatenated Skip Connection •
Convolution •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
Inverted Residual Block •
Max Pooling •
Pointwise Convolution •
ReLU •
RMSProp •
Sigmoid Activation •
Squeeze-and-Excitation Block •
Swish •
U-Net
