## 5 Conclusions  
The recognition effect of network on the gestures of motion blur was researched in this article. In the process of human-computer interaction, the phenomenon of image motion blur is produced by the movement between the hand and the camera. The motion blur affects the network recognition and is not easy to avoid. To improve the speed and accuracy  
Figure 17: Identification time sampling diagram of three models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Detection Accuracy** & **mAP** & **Average Detection Time** \\ \hline YOLOv4 & 89.35\% & 88.48\% & 3.19s \\ YOLOv4-gesture & 96.42\% & 94.57\% & 2.92s \\ YOLOv4-motion-blur-gesture & 97.79\% & 96.76\% & 2.68s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average detection accuracy, mAP, and average detection time of three models. of motion blurred gestures recognition, the research of YOLOv4 algorithm was studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 model is used to remove the motion blur of the gestures in YOLOv4 network input pictures. The K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP spatial pyramid pooling structure are added to YOLOv4 model to improving the efficiency of network learning. The dataset for network training is designed for the human-computer interaction in aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. The network structure optimization is obtained through experimental comparison. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. Based on the original data set used in model training, the augmented simulation under aerospace conditions was carried out to complete the expansion. The YOLOv4-gesture model and YOLOv4-motion-blur-gesture model were compared with the YOLOv4 model by drawing the ROC curves, PR curves, and time curves for the image detection and mAP histogram in the experiment. The proposed model has a stable recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The experimental results show that the YOLOv4-motion-blur-gesture model is reasonable and can be better adapted to the real-time human-computer interaction application. The YOLOv4-motion-blur-gesture model proposed in this article can well meet the motion blur gesture recognition in the real-time interactive environment. The model proposed in this article realizes the real-time recognition of blur images with high accuracy without much change in the detection speed. The radiation, space station turbulence, and hand motion blur always present in the aerospace environment. In the harsh space operation environment, it can realize high inclusive gesture recognition. The model also greatly improved the training speed and can carry out targeted training for specific application environment to get better results. Further research should be based on the extended data set of simulated aerospace environment to carry out the simulation experiments in special environments. The research results of this article can not only provide a technical basis for the multimodal human-computer interaction of electrically driven large-load-ratio multi-legged robot in interstellar exploration, but also can be applied to detection and interaction scenes in other complex environments with high real-time requirements. Conceptualization, H.Z.