## 2 Related Work  
With the emergence of the concept of \"unmanned substation rooms,\" intelligent devices, such as robotic arms and robots, are increasingly utilized in various scenarios for power distribution room inspections [11; 12]. Intelligent inspection robots offer greater diversity compared to traditional robots by incorporating capabilities such as human-machine interaction, automatic navigation, and environmental perception alongside mechanical actions. One notable example is Boston Dynamics' BigDog, introduced in 2005 [13]. BigDog represents the world's first robot capable of traversing rough terrain while maintaining stability under external forces and heavy loads. Boston Dynamics has since expanded its portfolio to include quadruped robots such as LS3 and the latest version of WildCat, SpotMini [14]. Moreover, some four-leg robots have developed into bipedal walking robots for more general applications [15; 16]. These robots have transitioned from laboratory settings to real-world applications, exhibiting advancements in coordination, flexibility, and functionality. Images of robots developed by Boston Dynamics are presented in Figure 3 [17]:  
The Unitree B1 industrial quadruped robot depicted in Figure 4, developed by Unitree Robotics, is capable of meeting the load demands of specialized and complex operations. With advancements in computer vision and deep learning, the real-time detection and accuracy of position prediction using deep learning target detection algorithms have been significantly enhanced. These improvements provide reliable data to support the robot's inspection functions. In this study, the Go1-edu quadruped robot developed by Unitree Robotics is employed. Designed for educational and scientific research purposes, this quadruped robot boasts a range of advanced functions and features that align with the research requirements of this study. A structural diagram of the Go1-edu is presented in Figure 5.  
Figure 3: Quadruped robots released by Boston Dynamics. Meter recognition is a technology that relies on pattern recognition, and initially employed several algorithms. In 1994, the Sablating R team pioneered number recognition for pointer meters by defining parameters such as the size, position, and shape of the pointer instrument. They utilized techniques such as feature extraction, angle calculation, image correction, straight line detection, and the Hough transform to achieve digital recognition of meters [18]. However, the accuracy of the extracted image features is inadequate, leading to unsatisfactory image recognition that fails to meet the demands for meter reading accuracy in practical applications. With the emergence of the convolutional neural network [19], image recognition technology began to experience a significant breakthrough, particularly with the deep learning model AlexNet, which has been widely used across multiple fields and has achieved excellent results. This development opened a new avenue for automatic meter reading research: extracting image features of pointer meters using deep learning methods [20]. In 2000, [PERSON] et al. proposed the use of computer vision for the automatic calibration of meter positions and values, achieving significantly lower recognition errors compared to human visual recognition [21]. The application of deep learning algorithms has notably enhanced the accuracy of pointer meter readings when compared to traditional machine learning algorithms. For the recognition of digital meters, the traditional hand-copying reading method is inefficient and prone to data errors [22]. Consequently, in recent years, researchers have increasingly turned to computer vision processing technology to achieve digital meter reading. Typically, these methods involve capturing images with a camera, preprocessing the images, and then extracting digital meter numbers through image processing techniques. OpenCV stands out as a mature and widely utilized technology in the field of image recognition [23].