## 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
blur-gesture model. The most comprehensive effect of the YOLOv4-motion-blur-gesture model is represented by the cut-off point P. In Figure 15, the classification effect of the YOLOv4-motion-blur-gesture model is better than YOLOv4-gesture and YOLOv4 model under any limit value condition. The best effect is the YOLOv4-motion-blur-gesture model, it is closest to the (0,1) point. The ratio of the truly positive samples in the predicted positive results is represented as the accuracy rate (Precision). The correct predicted sample ratio in the original sample is referred to as Recall. To facilitate the evaluation of the comprehensive ability of the model, the straight line passing through the point (0,0) at an angle of 45\({}^{\circ}\) to the horizontal axis is drawn in Figure 16. The intersection between the PR curve and diagonal line is the classification result under the comprehensive consideration of the network. The relationship between the precision rate and the recall rate is represented by the PR curve. The network PR graph and mAP values of three models are shown in Figure 16. In Figure 16, it can be concluded that the YOLOv4-motion-blur-gesture model has the best effect. The effect of YOLOv4 is relatively poor. In target detection, the average accuracy (AP) is the area under the PR curve. The mAP value of the YOLOv4-motion-blur-gesture is higher than other two models, reaching 96.76%. In the real-time monitoring reference environment, the speed of network target recognition also affects the final work efficiency. The improved YOLOv4-gesture and YOLOv4-motion-blur-gesture models are used to recognize 100 randomly selected gesture pictures. The identification time sampling diagram of the three models is shown in Figure 17. The  
Figure 16: PR graph of three models. Figure 15: ROC curve graph of three models. average recognition time of YOLOv4-gesture and YOLOv4-motion-blur-gesture models is longer than the YOLOv4 model. Moreover, whether the input is a blurred image or a clear image, the performance of YOLOv4-motion-blur-gesture model is more stable. In a good interactive environment, the YOLOv4-motion-blur-gesture model may take longer time than YOLOv4 model. However, in the aerospace space with harsh environment, blurred images exist widely, the YOLOv4-motion-blur-gesture model proposed in this article has higher reliability. It can be seen from Figures 14-17 that the YOLOv4-motion-blur-gesture model proposed in this article has made great progress in motion blurred gesture detection. The accuracy, precision, recall, mAP value of target detection, and the detection speed of motion blurred image are improved in the YOLOv4-motion-blur-gesture model. To express the improvement effect obtained by the YOLOv4-motion-blur-gesture model more intuitively, the mAP value and the average detection time of the three models are shown in Table 5. In Table 5, the average detection accuracy and the mAP value of the YOLOv4-gesture model are improved by 7.07% and 6.09%, respectively, compared with the YOLOv4 model. The YOLOv4-gesture model also reduces the average detection time of targets by 0.27 s. Compared with the YOLOv4-gesture model, the average detection accuracy and the mAP value of YOLOv4-motion-blur-gesture model are improved by 1.37% and 2.19%, respectively. The YOLOv4-motion-blur-gesture model also reduced the average detection time of the target by 0.24 s. Compared with the YOLOv4 model, the YOLOv4-motion-blur-gesture model has a greater improvement. The average detection accuracy and the mAP value were improved by 8.44% and 8.28%, respectively. The YOLOv4-motion-blur-gesture model also reduced the average detection time of the target by 0.51 s.