## 6 Experimental Results and Discussion
### Comparison of Feature Extraction Module Structures  
The GhostNet series networks are lightweight networks designed for image classification, which may not be optimal for radar imagery recognition. Therefore, we designed TG2 Net specifically to handle radar imagery. To demonstrate the advantages of TG2 Net, we conducted a longitudinal comparison with the GhostNet series networks, as shown in Table 3. Here, the accuracy is based on TD as the input.  
In this subsection and the subsequent experiments, the evaluation of lightweight network models is based on two categories of metrics. The first category encompasses model efficiency indicators, including the number of trainable parameters, FLOPs, and inference time, which assess the computational cost and operational efficiency. The second category includes performance metrics, namely accuracy, precision, recall, and F1 score, offering a comprehensive assessment of the model's classification capabilities.  
By comparing TG1 Net and TG2 Net, as well as GhostNetV1-0.5 and GhostNetV2-0.5 in Table 3, it can be observed that adding DFC attention significantly improved the network performance, at the cost of a slight increase in the number of parameters and FLOPs. This improvement was mainly attributed to the DFC's ability to capture the dependencies between pixels in distant spatial locations. However, there was an exception: the accuracy of GhostNetV2 was approximately 0.7% lower than that of GhostNetV1. We speculate that  
\begin{table}
\begin{tabular}{c c c} \hline
**Action Code** & **Action Name** & **Action Description** \\ \hline A & Walk & Move away while walking \\ B & Run & Move away while running \\ C & Punch & Punch alternately \\ D & Kick & Kick legs alternately \\ E & Step & Step alternately \\ F & Bend & Process from standing to bending \\ G & Sit & Process from standing to sitting \\ H & Squat & Process from standing to squatting \\ I & Stand & Process from squatting to standing \\ \hline \end{tabular}
\end{table}
Table 2: Action description.  
this may have been due to the fact that the radar spectrogram data were better suited for simple, fast convolution operations that extract local information, and the addition of DFC introduced redundant computation, leading to a decrease in the generalization ability.  
To further investigate the impact of the model complexity on the recognition performance, we reduced the scale of GhostNetV1 and GhostNetV2 to half of their original size and added or removed a layer from TG2 Net. The comparison in Table 3 reveals that, except for GhostNetV1-0.5 and TG2 Net, the difference in accuracy among the remaining models was not significant. This suggests that by using GhostV2 convolution, it was possible to explore an optimal lightweight feature extraction module TG2 with minimal parameter costs. TG2 achieved 97.99% accuracy with only a 6-layer structure, 0.12 M parameters, and 52.4 M FLOPs. This remarkable performance is attributed to the TG2 structure, which draws inspiration from ResNet18. A key factor is the similarity to ResNet18 in the number and arrangement of bottleneck blocks. Thus, TG2 can be viewed as an improvement of the successful classic model ResNet18, leveraging GhostV2 convolution to create a lightweight network. The superior performance of ResNet18 on the dataset used in this paper will be demonstrated in Section 6.4.