# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 2 Materials and Methods
### SAR Image Recognition Algorithm  
#### 2.4.1 Lightweight Convolutional Neural Networks  
While the traditional convolutional neural network leads to the process of training the model, which occupies a lot of time and memory space, a lightweight convolutional neural network, with the advantages of small model volume, high accuracy and less computation, can be used to construct an object recognition algorithm. Software can be integrated into resource-limited embedded and mobile devices, which meets the actual needs of security scene. Figure 10: (**a**) Reconstruction of the image; (**b**) gamma transform algorithm with coefficient of 2.4 is used to process the original image; (**c**) the original image is linear stretching algorithm after gamma algorithm. Figure 9: (**a**) Gamma transform algorithm with coefficient of 2.4; (**b**) linear stretch algorithm. Lightweight convolution neural networks include MobileNet, ShuffleNet, GhostNet and other lightweight models. MobileNet and ShuffleNet, respectively, use point-wise convolution and channel shuffle to achieve the purpose of feature communication, which realizes the fusion of features between different groups. GhostNet adopts a different approach, which is based on a group of original feature images and uses linear transformation to obtain more features that can excavate the useful information from the original features. The original feature and the linear transform feature are spliced together to enlarge the feature image. By redefining the convolution rules, the lightweight model can extract image features efficiently with a shallow network structure and a few parameters. The active millimeter wave imaging system can obtain single-channel images, which contain less information than light images, and the contrast between target contour and background is not obvious. More importantly, active millimeter wave images will have varying degrees of virtual shadows due to their imaging principle, which will have a great impact on the classification effect. Based on these characteristics, this paper will use the convolution neural network module with the method of experiment and attention mechanism. On the one hand, the convolutional neural network has strong feature extraction ability; on the other hand, the attention mechanism is used to obtain more details of the target to be concerned, so as to suppress interference information in millimeter wave images and improve the efficiency and accuracy of feature extraction. The data set consists of 250 SAR images in 10 categories, and the training set and the validation set are divided in a ratio of 3 to 2, all of which are scanned at a distance of 90 mm from the radar. Three representative lightweight networks are proposed in this paper: (1) ShuffleNetV2; (2) MobileNetV3; (3) GhostNet_ResNet56 based on GhostNet, which have been repeated for five rounds of verification, and the prediction accuracy is the average of the five rounds of experiments. The images are firstly normalized, and the number of image channels input to the neural network is adjusted to adapt to the characteristics of the grayscale images. During the training, the learning rate of all networks is set as 0.01, the batch size is 16, and the epochs are 30. * ShuffleNetV2  
The ShuffleNetV2 network improves the ShuffleNetV1 network. Firstly, the convolution step size is selected. For the bottleneck block with convolution step size of 1, the input features are first divided into two parts according to channels. The result is entered into two branches, one of which does not take any action to reduce the number of parameters and computational complexity, and the other branch does not take grouping convolution to reduce the memory access cost. For the subsampling building block with convolution step size of 2, the number of feature channels is doubled. In ShufflenetV2, a layer of \(1*1\) convolution is added before the average pooling layer, to further mix features.