# 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
Concat module is used to replace the original addition of each element to reduce the computational complexity, and Channel Shuffle module is added to increase the information communication between channels [20]. The ShuffleNetV2 convolutional neural network flowchart is shown in Figure 11.  
The SAR images are recognized through ShuffleNetV2 network, and the accuracy of validation set is 84.55%. This low accuracy may be due to the slower convergence speed of the network in a limited number of epochs. Moreover, as the number of epochs increases, the accuracy will be improved to a certain extent. * MobileNetV3 combines the advantages of MobileNetV1 and MobileNetV2. At the convolution level, MobileNetV1 introduces the deep separable convolution, decomposes the standard convolution into deep convolution and point-by-point convolution, and MobileNetV2 introduces the linear bottleneck and backward residual structure in the network structure. On this basis, MobileNetV3 introduces a squeeze-excitation (SE) attention mechanism in the bottleneck structure. The SE module automatically obtains the importance degree of each feature channel through learning, which enhances the useful features according to the importance degree and inhibits the features that are less useful to the current task [21]. The SAR images are recognized through MobileNetV3 (SE) network, and the accuracy of validation set is 98.18%. * GhostNet  
GhostNet proposes a novel Ghost module that replaces ordinary convolution and can generate more feature images with fewer parameters. Unlike ordinary convolution, the Ghost module contains two steps. In the first step, the feature image of input is convolved to obtain the feature image with half the channel number of ordinary convolution operation. In the second step, linear transformation is used to obtain another part of the feature image generated in the first step. Finally, the two groups of feature images are stitched together to generate the final feature image. The ghost module can replace the ordinary convolution to reduce the computational cost of the convolution layer [22]. The GhostNet convolutional neural network flowchart is shown in Figure 12.  
Figure 11: ShuffleNetV2 network structure. The SAR images are recognized through the GhostNet_ResNet56 network, and the accuracy of the validation set is 95.45%. This accuracy is significantly higher than that of ShuffleNetv2 but a bit lower than that of MobileNetV3. However, in terms of model parameters and memory usage, GhostNet_ResNet56 is better than MobileNetV3. Thus, GhostNet_ResNet56 is suitable for the classification task of millimeter wave images. In order to further improve the accuracy of networks, a confusion matrix is used to reflect the accuracy of image classification more clearly, as shown in Figure 13.  
It can be seen from the confusion matrix that the GhostNet_ResNet56 network is not good at distinguishing between key, pliers, knife, ax, etc., which leads to lower prediction accuracy. Based on the above three basic network models, it can be seen that the MobileNetV3 convolutional neural network with the introduction of the SE attention mechanism model has the highest prediction accuracy. Therefore, the squeeze-and-excitation (SE) and selective-kernel (SK) attention mechanism modules are used to improve the existing classification network.