## 1 Introduction  
With the research and development of robotics, machine vision has been able to replace the human eyes for certain target recognition and judgment. The visual recognition is very important for robots, especially for the electrically driven large load-ratio multi-legged robots in planetary exploration [1, 2]. The intelligent target recognition of the robots can be realized by applying deep learning in the robot vision task. Gesture recognition is widely used in the interaction with robots in aerospace space with complex and harsh conditions. By making different command gestures to the robots, the command transmission can be completed. Before the deep learning is widely adopted, the feature representation in the traditional target detection is generally based on the manual design of feature detectors [3; 4], such as SIFT [5], HOG [6], and so on. These traditional target detections are based on the content of a picture, which is difficult to design, low in portability, and low in recognition accuracy. Taking the face detection in the picture as an example, the hand-designed features usually need to repeatedly adjust the relevant parameters to fully detect the target in an ideal environment. Early gesture recognition tasks require the users to wear data gloves to perceive and complete the recognition. For example, the [PERSON] team designed a gesture recognition interface WUW [7] based on the wearable video devices. The interface uses a wearable camera to capture the gesture images. The gesture images can be converted into the input signal operation commands. Although the recognition efficiency of that method is high, it is inconvenient to use. The equipment is heavy and cumbersome to wear. With the continuous development and optimization of wearable devices, researchers also make them into the shape of wristbands and finger rings. However, their recognition and perception are still based on the transmission of wires. There are still certain difficulties in popularization and use of wearable devices [8; 9]. For a more convenient and natural user experience, the gesture recognition based on the machine vision has developed rapidly. To complete non-contact gesture recognition, the study of gestures is performed through the computer by researchers. The gesture images are collected by camera. By performing the gesture segmentation, gesture tracking, and gesture feature extraction, the information can be transmitted by the computer. Based on a screen-printed conformal electrode array, [PERSON] et al. put forward a wearable surface electromyography biosensing system [10]. That system has sensor adaptive learning capabilities which can be applied to real-time gesture classification. [PERSON] et al. designed a virtual reality interactive glove by using an actuator, which is very light and easy for users to wear [11]. [PERSON] et al. presented a skin-patchable magneto-interactive electroluminescent display, which is capable of sensing, visualizing, and storing magnetic field information to realize the 3D motion tracking [12]. [PERSON] et al. proposed a hand gesture recognition system by using near-infrared imagery. The hand gesture characterizes can be recognized by the system directly [13]. [PERSON] et al. used the CNN in many recognition and classification applications to achieve the machine learning [14]. [PERSON] et al. performed the method research of human gesture recognition by integrating the visual data and somatosensory data from the skin-like stretchable strain sensors made from single-walled carbon nano-tubes [15]. That method is neither limited by the quality of the sensor data nor the incompatibility of the datasets. It can maintain the recognition accuracy under the nonideal conditions of the high image noise, underexposure, or overexposure. [PERSON] et al. utilized YOLO to complete the recognition and positioning of human actions, which can accurately identify and locate the group frames or even single frames of human movements in the video [16]. [PERSON] et al.