## 1 Introduction  
To ensure the optimal functioning of substation facilities, real-time monitoring of the operational status of arresters is crucial. Owing to their consistent data display, straightforward structure, and superior anti-interference capabilities, pointer instruments are extensively utilized in substations. However, traditionally, the reading of instrument scales typically necessitates manual operations at predetermined intervals, a method that is not only labor-intensive but also susceptible to omissions and errors. With advancements in technology, intelligent scale recognition systems for instruments are increasingly supplanting manual operations, demonstrating the capability to efficiently and accurately carry out the tasks of reading and recording scales in substations. Currently, in the research of scale recognition for instruments, researchers have proposed solutions from different perspectives. [PERSON] et al., have developed an innovative method that employs computer vision techniques for the detection of pointers and the recognition of indicator numbers. In this method, the characteristic features of the instrument's pointer are extracted from an image that has undergone preliminary processing. This is performed by applying the rules of Freeman chain code encoding. The indicator number associated with the pointer is determined by analyzing its angular position [1]. [PERSON] et al., have introduced an optimized three-point-based circle determination method for calibrating the dial center and segmenting the dial, and they also propose an improved angle method to facilitate the automatic reading recognition of pointer instruments [2]. [PERSON] has created an automated system for recognizing pointer meters that capitalizes on line scan vision technology. This system employs the light-spot centroid algorithm to accurately determine the central point of the meter's pointer. Trials have demonstrated that this method for extracting data from pointer meters exhibits remarkable resilience against various disturbances [3]. [PERSON] et al., have introduced a highly precise and sturdy algorithm for the automatic reading of pointer meters. Their innovative approach involves a new method for setting the binarization threshold, which is based on symmetry levels to segment the region of the pointer, successfully overcoming issues related to the shadow of the pointer [4]. [PERSON] et al., have introduced a method for recognizing pointer instruments that uses Fourier transform to speed up convolution processes and improve cosine similarity measures. By utilizing statistics of grid motion, this technique efficiently removes incorrect match points and assists in the refined alignment of the image, thereby greatly improving the speed of identification for pointer instruments [5]. [PERSON] et al., have developed an algorithm for reading dial gauges based on coordinate positioning, which tackles the challenges posed by irregular lighting, intricate dial backgrounds, and the interference from damping fluids in the gathered images of the instruments. The results of their experiments confirm that this algorithm successfully identifies the circle's center and the gauge pointer, enabling the automatic acquisition of readings [6]. [PERSON] et al., have presented a smart approach for reading indications on pointer meters and automating data gathering, utilizing machine vision combined with wireless sensor network (WSN) technology. Findings indicate that their method for detecting pointers shows enhanced robustness and superior resistance to interference when compared to traditional image segmentation and skeletonization techniques [7]. [PERSON] et al., have proposed a real-time system for reading single-point analog meters, employing the YolactEdge instance segmentation framework, which has been executed and operationalized on the Jetson Xavier NX edge computing device [8].