# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 1 Introduction  
With the continuous development of society, people's demand for electricity is also increasing. In order to reduce the loss of a power outage caused by power equipment failure and ensure the safe operation of power equipment, it is necessary to conduct regular inspections of power equipment. Equipment is usually inspected via manual inspection, where staff regularly check the equipment and record the results according to the designated route. This method has many issues, such as prolonged inspection time, low inspection efficiency and high omission rate. In recent years, with the continuous breakthrough of technology, more and more machine learning technologies have been applied to various industrial fields. For example, the physics-informed neural network (PINN for short) [1] is a type of neural network used to solve supervised learning tasks. Compared with pure data-driven neural network learning, PINN imposes physical information constraints in the training process, and so it can learn a model with more generalization ability and fewer data samples. [PERSON] et al. [2] applied the PINN in the power system to achieve an accurate prediction of rotor angle and frequency based on small sample conditions. [PERSON] et al. [3] proposed a resonance-informed deep learning (RIDL) strategy for fast and accurate prediction of the optical response of ultrahigh Q-factor resonances. By incorporating resonance information into deep learning algorithms, the RIDL strategy achieves highly accurate predictions of reflection spectra and photonic band structures while using relatively small training datasets. Additionally, contributions from machine learning techniques can be seen inobject detection and other areas. For instance, You Only Look Once (YOLO for short) [4] is an end-to-end target detection algorithm based on neural networks. YOLO has good real-time and accurate detection, making it popular in the detection field. [PERSON] et al. [5] realized the real-time detection of tiny defects in kiwi fruit based on an improved YOLOv5 [6] algorithm. [PERSON] et al. [7] realized the automatic grading of apples using the improved YOLOv5 algorithm, which solved the problems of low grading accuracy and slow grading speed in the apple grading process. Furthermore, [PERSON] et al. [8] used image enhancement based on multi-scale pyramid fusion and the MobileCenterNet model to realize the real-time detection of underwater crabs. [PERSON] et al. [9] realized the text recognition task of the pill box based on DBNet and the CRNN [10]. In the field of power industry inspection, machine learning technology has also been widely used. For example, [PERSON] et al. [11] located the position of the pointer by searching the minimum bounding rectangle (MBR) of the pointer region, and then used the connection between the center of the pointer to the center of the dial as the pointer and calculated the indication value corresponding to this pointer according to the angle method. [PERSON] et al. [12] proposed a meter recognition and reading method based on the YOLOv3 [13]. This method used the trained YOLOv3 model to detect and identify meter types. The dial of the pointer meter is detected by the Hough circle detection algorithm and the scale and pointer are extracted using the image processing method. This method calculated the angle between the 0-scale line and the pointer to convert the reading of the pointer meter. For digital instruments, this method obtained digital areas via image morphology and recognized them using a trained support vector machine (SVM). [PERSON] et al. [14] used an SVM to identify the extracted digits. [PERSON] et al. [15] used Mask R-CNN [16] to locate the dial position of a meter. Next, all of the digit scale regions were identified using RCNN [17] and the pointer was extracted using the region growing method. The reading was then based on the position of the pointer and the two closest scale marks found by searching through the circular scale. [PERSON] et al. [18] used a cascade approach toward meter reading.