## 4 Experimental Evaluation
### Comparison with Other Gesture Recognition Methods  
To verify the effectiveness of the proposed method in detecting gestures, representative lightweight networks ShuffleNetv2-YOLOv3 [43] and YOLOv3-tiny-T [42]; the complex background gesture recognition method used in the literature [50]; and YOLOv7 (the improved version of YOLOv5) [54], as well as the original YOLOv5l algorithm, were selected for comparison experiments with the proposed method. The EgoHands dataset was used for the experimental data, and the experimental results are shown in Table 1.  
For the YOLOv3-tiny-T algorithm, the method used \(1\times 1\) convolution after each of the 6 maximum pooling layers in the YOLOv3-tiny network to further halve the output channels for fast gesture detection. As this method reduced the network width, resulting in poorer feature extraction and difficulty in extracting gesture features in complex backgrounds, it achieved only 66% mAP, a 7.5% reduction compared to the YOLOv5l algorithm, despite its good model size, GFLOPs, and FPS. The ShuffleNetv2-YOLOv3 algorithm used a lightweight ShuffleNetv2 network to replace the backbone in the original YOLOv3 network, and used a CBAM attention mechanism after the backbone network to improve the recognition accuracy and speed. Although the method achieved a recognition speed of 69 FPS, it was difficult to achieve better recognition results using this method due to the presence of a large amount of interference in forms such as occlusion in the complex background gesture images. Its accuracy, completeness, and mAP values were lower than those of the other comparison methods, except the YOLOv7 algorithm. The YOLOv7 algorithm proposed the use of the E-Elan extended efficient layer aggregation network to extract image features. It also used the structural reparameterization method to merge multi-branch network structures to improve the recognition speed, which had a certain effect, as can be seen from the fact that this method had the highest model size and GFLOPs, but its FPS reached 68. However, the mAP value of the method was only 59.6, which was lower than all of the comparison methods, showing that the use of extended efficient layer aggregation networks does not overcome the challenges present in complex background gesture detection and recognition. This can be demonstrated in subsequent experiments using different backbone networks. The method used in the literature [50] improved network performance by adding a densely connected layer to the deeper network at the lower resolution of YOLOv3 to enhance image feature transfer and reuse for end-to-end complex background gesture recognition. Although the mAP value of this method reached 68.5%, it was still lower than the 75.6% achieved by the proposed method. Moreover, the memory footprint of this method was as high as 152 GFLOPs, the model size reached 122.6 MB, and the FPS was only 31. This method is not conducive to practical deployment applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **FPS** &
\begin{tabular}{c} **Model** \\ **Size/MB** \\ \end{tabular} & **GFLOPs** \\ \hline YOLOv3-tiny-T & 95.1 & 90.3 & 66.0 & 227 & 9.4 & 7.2 \\ ShuffleNetv2-YOLOv3 & 92.8 & 78.8 & 61.6 & 69 & 49.1 & 42.5 \\ YOLOv5l & 96.2 & 92.2 & 73.5 & 54 & 92.8 & 108.2 \\ YOLOv7 & 92.6 & 83.8 & 59.5 & 68 & 74.8 & 105.1 \\ literature [50] & 96.1 & 90.1 & 68.5 & 31 & 122.6 & 152.0 \\ proposed algorithm & 97.8 & 94.4 & 75.6 & 64 & 88.3 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of different method on the EgoHands dataset. Although the YOLOv5I algorithm achieved a mAP value of 73.5, the network was too complex, the feature reuse rate was not high, and the recognition speed was only 54 FPS, values which do not represent a good trade-off between recognition accuracy and speed.