## 6 Experimental Results and Discussion
### Comparison with Other Models  
As shown in Table 7, we compared TG2-CAFNet with several mainstream models to validate its performance. Overall, when the TDM was used as input, the accuracy was generally higher than when the RTM was used. Regardless of the input type, DenseNet121 and ResNet18 achieved the highest recognition accuracy, primarily due to their complex deep network structures, which allowed them to capture more detailed features and improve the classification results. However, their large number of parameters makes them unsuitable for deployment in practical applications. In the comparison of lightweight models, we selected four classical lightweight architectures. In terms of parameters and FLOPs, MobileNetV3-Small-0.75 and ShuffleNetV2-0.5 performed similarly to TG2 Net. However, MobileNetV3-Small-0.75 performed poorly in terms of accuracy, limiting its applicability in scenarios with high accuracy requirements. While ShuffleNetV2-0.5 excelled for inference time, it fell slightly behind TG2 Net in other metrics. EfficientNetLite0 and MobileViTV3-0.5 were inferior to TG2 Net in both lightweight design and accuracy.We also compared a CRNN, a temporal model. Although the CRNN had a relatively small number of parameters and FLOPs, it suffered from a longer inference time and lower accuracy compared to TG2 Net. Finally, TG2-CAFNet, by fusing both RTM and TDM features, further boosted the recognition accuracy by approximately 1%. Although it did not have the best parameters or FLOPs among all models, TG2-CAFNet achieved notable accuracy improvements by sacrificing a modest increase in parameters and FLOPs, meaning the model remains suitable for deployment. While its inference time may not be its strongest suit, the focus of this research was not real-time performance, but deployability and accuracy improvements.  
In summary, TG2-CAFNet leverages GhostV2 convolution to construct a radar spectrogram feature extraction module, TG2, with a significantly lower parameter count and FLOPs compared to existing lightweight models. We believe that the lightweight nature and high recognition accuracy of TG2 for radar spectrograms can mainly be attributed to redundant features and DFC attention. RTMs and TDMs are representations of human actions in the range-time domain and time-Doppler domain, respectively. These representations are abstract and contain a lot of details. Redundant features enrich feature representation and help capture complex range-time and time-frequency relationships. The introduction of DFC further enhances the model's focus on the spatial information of radar spectrograms. Then, multi-domain fusion technology is used to further improve the model's performance. Traditional fusion methods do not allow for steady performance improvements, so the non-linear fusion method AFF is introduced, providing a dynamic adaptive weighting fusion approach. Meanwhile, the use of CA extends the weighted fusion to spatial distribution, which is of significant importance for radar spectrograms with a clear physical meaning.  
Figure 13: Two confusion matrices of TG2-CAFNet used for detailed error analysis. The red rectangles highlight the groups of similar actions where misclassifications and omissions primarily occurred.