## 4 Network Training and Model Effect Analysis
### Effect Analysis of Motion Blur Gesture Detection
#### 4.2.2 Motion Blur Gesture Detection Accuracy and Recognition Speed Effect  
To obtain more reliable clustering results, the K-means++ clustering algorithm is used to replace the original K-means clustering algorithm. The prior frame sizes calculated under different clustering algorithms are used for network training. The frame selection effects of the two methods are compared to draw a conclusion. The network detection effect chart under the k-means and k-means++ clustering algorithms is shown in Figure 14. The experiment compared the frame selection effects of four different gestures. The frame selection integrity of victory gesture and the accuracy of target recognition are the most different. The improvement of the priori boxes clustering algorithm achieves good results. In Figure 14, the using of k-means++ clustering algorithm is not prone to the invalid selection. The k-means++ clustering algorithm has higher frame selection integrity for target gestures. In contrast, the more complete target gesture selection effect can be obtained by using the k-means++ clustering algorithm to reset. The training effect of the network is improved with the improvement of the superior target selection degree. The above improvements play a important role in the recognition of specific targets. Figure 13: Motion blurred image processing effects of four different gestures compared in the experiment: (**a**) unprocessed motion blurred gesture images; (**b**) motion blurred image processed by the DeblurGanv2 model. To verify the effectiveness of adding the SPP and CBAM module in the YOLOv4 model, the ablation experiments are carried out in this article. The SPP module and CBAM module are added into the YOLOv4 to build the YOLOv4-SPP model and YOLOv4-CBAM model. The YOLOv4-gesture model is obtained by collaborating with the YOLOv4-SPP model and YOLOv4-CBAM model. The three models are trained with the same parameters. The changes of mAP of the three models during training are shown in Table 4. Based on Table 4, it can be concluded that the addition of the SPP and CBAM modules improved the performance of YOLOv4 network. When the epoch is 100, the mAP value of the YOLOv4-SPP model improved by 2.37%, the YOLOv4-CBAM model improved by 4.96%, the YOLOv4-gesture model improved by 8.28%. Therefore, the proposed YOLOv4-gesture model is effective. The performance effect of the network under different task requirements can be reflected by the ROC curves. In different application tasks, different cut-off points can be selected according to requirements to compare the performance of the model. The more forward the position is, the higher the precision is required. On the contrary, it requires higher recall. The ROC curves are drawn with the false positive rate of the horizontal axis and the true rate of the vertical axis. The threshold range of true positive rate is from 0 to 1. Therefore, the closer the curve is to the (0,1) point, the more comprehensive the network effect. The ROC curves of different structure networks are shown in Figure 15. The YOLOv4-motion-blur-gesture is the final network model of the experiment. To facilitate the evaluation of the comprehensive ability of the model, the straight line passing through the point (0,1) at an angle of 45\({}^{\circ}\) to the vertical axis is drawn in Figure 15. In Figure 15, P is the intersection point between the straight line and the ROC curve of YOLOv4-motion \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mAP (epoch = 50)** & **mAP (epoch = 75)** & **mAP (epoch = 100)** \\ \hline YOLOv4 & 60.33\% & 73.66\% & 88.48\% \\ YOLOv4-SPP & 64.76\% & 78.21\% & 90.85\% \\ YOLOv4-CBAM & 64.83\% & 83.74\% & 93.44\% \\ YOLOv4-gesture & 65.35\% & 85.57\% & 96.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mAP values of the three models under different epochs. Figure 14: Target frame selection effect comparison chart: (**a**) network detection effect under k-means clustering algorithm; (**b**) network detection effect under k-means++ clustering algorithm.