## 4 Experimental Evaluation
### Ablation Experiments  
In order to compare the effects of different backbone networks on recognition accuracy and speed, and to demonstrate the advantages of the backbone network used in this paper, experiments were conducted using different backbone networks based on the YOLOv5I algorithm, and the experimental results are shown in the first five columns of Table 2. It can be seen that, although using the ShuffleNetv2 network as the backbone network greatly reduced the model size and GFLOPs, its FPS did not increase, but rather decreased to 31, and its three indexes, namely the accuracy rate, the completion rate and the mAP, all decreased to different degrees. The accuracy metrics, such as mAP, when using the VoVNetv2 network [55] as a backbone network were not much different from the original YOLOv5I algorithm. However, the FPS using this method was the lowest, at 27, which cannot meet the real-time requirements, and the model size was also much larger. When using E-ELAN [54] as the backbone network, although speed metrics such as FPS were significantly improved, the accuracy metrics were reduced to varying degrees compared to the original YOLOv5I algorithm. Experiments showed that using ELAN as the backbone network not only improved the accuracy of the original YOLOv5 algorithm, but also significantly increased the speed of the model, reduced memory usage, and met the demand for real-time gesture recognition.  
To demonstrate the effectiveness of the utilized attention mechanism, the proposed method was used. The YOLOv5I+ ELAN + SSE was employed, with the sSE attention mechanism module [55] as a comparison, and the experimental results are shown in the last 2 columns of Table 2. The sSE attention mechanism module reduced the FC layer in the original SE attention mechanism module from 2 to 1 to avoid the loss of channel information due to a reduction in dimensionality. Table 3 shows that adding the sSE attention mechanism module did not achieve the desired effect, but instead reduced the value of each metric. On the other hand, adding the CBAM attention mechanism module improved the mAP by 0.8%, and also improved both the check-all rate and the check-accuracy rate, achieving a better trade-off between accuracy and speed despite the small difference in model size, FPS, etc. The experiments demonstrated that the addition of the CBAM attention mechanism module was able to improve the model's detection and recognition performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **FPS** & **Model** & **GFLOPs** \\ \hline YOLOv5I & 96.2 & 92.2 & 73.5 & 54 & 92.8 & 108.2 \\ YOLOv5I + ShuffleNetv2 & 92.2 & 89.4 & 69.3 & 31 & 47 & 42.1 \\ YOLOv5I + E-ELAN & 97.0 & 90.5 & 73.5 & 70 & 73.5 & 76.8 \\ YOLOv5I + VoVNetv2 & 96.8 & 92.3 & 73.6 & 27 & 103.2 & 107.7 \\ YOLOv5I + ELAN & 97.3 & 93.5 & 74.8 & 71 & 84.1 & 95.1 \\ YOLOv5I + ELAN + SSE & 97.2 & 89.8 & 72.7 & 58 & 88.5 & 96.9 \\ YOLOv5I + ELAN + CBAM & 97.8 & 94.4 & 75.6 & 64 & 88.3 & 97.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different modules on the EgoHands dataset.