# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 3 Results and Discussion  
According to the results of the confusion matrix, this paper uses SE and SK attention mechanism to optimize MobileNetV3, ShuffleNetV2 and GhostNet lightweight convolutional neural networks. The results are shown in Table 4.  
Figure 14: SE attention mechanism optimizes GhostNet_SEResNet56 network. Where Madd represents the number of operations multiplied first and then added, FLOPs represent the number of floating point operations and MemR + W represents the total memory space occupied by the model. Table 4 shows that the prediction accuracy of the three series networks was significantly improved after the optimization of SE and SK attention mechanism. Although the introduction of the attention module into the SAR image recognition algorithm will increase the network load slightly, it is a tolerable range. The Madd, Parameters, FLOPs and MemR + W of ShuffleNet series are all higher than those of the other two models, which indicates that the number of calculation amount of the model is the largest and occupies the most memory space, but its prediction performance on SAR image datasets is worse than that of MobileNetV3 series and GhostNet series. By comparing GhostNet_SERResNet56 and MobileNetV3_SK, the prediction accuracy of GhostNet_SERResNet56 is the same as MobileNetV3_SK; the Madd and FLOPs of GhostNet_SERResNet56 are slightly higher than MobileNetV3_SK, but parameters and MemR + W are significantly lower than MobileNetV3_SK, indicating that GhostNet_SERResNet56 optimized by SE attention mechanism can play the greatest advantages within the most limited resources. The confusion matrix of GhostNet_SERResNet56 algorithm is shown in Figure 15a. GhostNet_ResNet56 is optimized by the SE attention mechanism. Compared with the network without attention mechanism, the network with attention mechanism can significantly improve the accuracy in few epochs. In addition, its convergence speed is significantly accelerated, and the oscillation effect of the tail is effectively weakened, as shown in Figure 15b. Comprehensively consider the classification accuracy of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object Recognition** & **Accuracy** & **Madd** & **Parameters** & **FLOPs** & **MemR + W** \\
**Algorithm** & **(Valid)** & **(MMadd)** & **(M)** & **(MFLOPs)** & **(MB)** \\ \hline ShuffleNetV2 & 84.55\% & 284.89 & 1.26 & 144.16 & 47.32 \\ \hline ShuffleNetV2\_SE & 87.27\% & 285.15 & 1.40 & 144.30 & 47.85 \\ \hline ShuffleNetV2\_SK & 89.09\% & 293.94 & 1.69 & 148.85 & 56.51 \\ \hline MobileNetV3(SE) & 96.36\% & 115.41 & 1.24 & 58.60 & 30.93 \\ \hline MobileNetV3\_SK & 98.18\% & 119.98 & 1.30 & 60.99 & 56.51 \\ \hline GhostNet\_ResNet56 & 95.45\% & 134.54 & 0.44 & 67.80 & 18.95 \\ \hline GhostNet\_SERResNet56 & 98.18\% & 134.55 & 0.45 & 67.81 & 18.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of prediction results and performance of three series lightweight neural network. Figure 15: **(a)** GhostNet_SERResNet56 confusion matrix on the SAR validation set; **(b)** comparison of prediction accuracy of GhostNet_ResNet56 network and GhostNet_SERResNet56 network with Epoch changes. neural network and its memory occupation, GhostNet_SEResNet56 are used as the object recognition network in this paper. In this paper, the millimeter wave imaging system can obtain the target SAR image at 90 mm. The number of virtual channels can be increased by increasing the antenna array, and then the longitudinal antenna aperture can be increased. The horizontal synthetic aperture can be widened by increasing the horizontal slide movement distance. The improved hardware can amplify the measured distance while maintaining the image resolution. In a realistic scenario, target containers and humans carrying targets can sway and move by more than the wavelength which will cause the image to blur. To solve this problem, the speed of the object can be measured first when the object is moving, and then the influence of the speed can be compensated in the imaging algorithm.