## 3 Dataset and Experimental Environment Construction  
### Dataset Establishment and Augmentation Optimization  
Due to the limitation of image extraction environment, the training data of network model has high similarity and poor robustness, which cannot produce good results in the training of a neural network [29]. In the actual gesture recognition process, the difficulty of network recognition and detection can be increased by different lighting conditions, background features, hand skin color, and the distance or angle between the camera with the acquisition camera. The human-computer interaction gesture data sets in the aerospace space are difficult to obtain and limited in quantity. To make the network better adapt to the aerospace operation environment, this article enhances the original data set. The data set is processed by distorting the color gamut channel, adjusting the image scale, clipping random image, adding random illumination, adjusting the image saturation, changing the image contrast, adding filtering and noise to obtain a model with higher generalization ability. The generalization ability of the network can be increased by such adjustment. The recognition effect of different gestures can be augmented and improved by using the dataset which merged the original dataset and the enhanced images. The gesture images before and after augmentation are shown in Figure 9.  
The neural network recognition is better when the target is complete, the background is clear, and the light is good. However, in the actual interaction process, the radiation, the fluctuation of the space station, and the movement of the hand can lead to the changes of the images collected by the robots. The information content of the image acquired will be reduced. The quality of network training and recognition is reduced by using the low information contained image. The motion blur of the collected image is caused by the movement of the hand during the actual image acquisition. The reduction of the amount of information contained in the image caused the poor network training effects. Therefore, to reduce the image motion blur, Wiener (minimum mean square error) filtering combined with bilateral filtering is selected to improve network performance. Figure 8: Structure diagram of the DeblurGanv2 model. Figure 9: Gesture images before and after augmentation: (**a**) gesture image before augmentation; (**b**) gesture image after augmentation. The Wiener filtering is an adaptive minimum mean square error filter, can process pictures with noise very well [30]. The algorithm assumes that the input signal is the sum of the useful signal and the noise signal. To find the estimate of the uncontaminated images, the minimum mean square error criterion is employed. The mean square value of the difference between the filter output signal and the desired signal should be as small as possible. The image is clearer while the mean square deviation between the output signal of the filter and the desired signal is the smaller [31]. The bilateral filtering is a Gauss filtering function which is based on the spatial distribution. The values of pixels farther away do not have much effect the pixel value on the edge. The weighted average method is adopted in the bilateral filtering. The intensity of the pixel is expressed by the weighted average of the brightness values of the surrounding pixels. The motion blur images can be improved by the Wiener filtering and the bilateral filtering. Since an image is composed of many pixels, the image processing is the processing of pixels. The Wiener filtering and the bilateral filtering have different processing methods for the pixels. The effect of filtering is different with different emphasis. For the motion blurred images in the dataset, different effects can be obtained by adding a single or different filter superposition.