# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 1 Introduction
The schemes of edge-based geometric matching and pyramidal gradient matching are used to detect the regions of interest. The mapping relationship between the function selector and the selector arrow, as well as that between the instrument meter and the pointer, are built and formulated to finally read the A-meter. This method is robust, but it can only be applied to specific instrument characteristics. [PERSON] et al. [12] applied the multiple dial gauge measurement method based on the inverse perspective mapping. The inverse perspective method was used to eliminate the influence of the angle on the readings. The visual inspection method was used to measure the processed image and obtain the meter reading. This method has high reading accuracy under the system which can control the brightness, but it has poor robustness in industrial fields where the measurement environment is complicated. In recent years, deep learning has been applied in this field and provided excellent results. [PERSON] et al. [13] used a method of using frame training and testing to detect pointers. The gray level sub-sampling along the tick marks generates a 1D signal and local maximums on the 1D signal that are used to calculate the meter reading. [PERSON] et al. proposed a new virtual sample generation technology in [14]. It solved the problem of generating a large number of images from a small number of real samples to train the recognition, and then obtained readings by analyzing the key information of the instrument images. [PERSON] et al. [15] improved the Mask-RCNN network and used PrRoIPooling instead of RoiAlign to improve the accuracy of segmentation. The improved Mask-RCNN was introduced to divide the dial and pointer area; then, fitted the pointer and judged the meter type at the same time; and, finally, used the angle method to calculate the reading. [PERSON] et al. [16] applied a novel algorithm for the adaptive detection of different dial gauges, which solved the problems of light affecting the accuracy and robustness of the shooting angle. It introduced deep learning technology to detect the scale value and text on the meter. After obtaining the text box, the center coordinates in the text box were adopted to correct the image to eliminate the influence of the shooting angle on the reading. A meter classification method based on Faster R-CNN was proposed by [PERSON] et al. in [17]. Image processing of the positioned dial gauge image was operated to calculate a more accurate pointer reading. Algorithms based on deep learning have a high accuracy in detecting instruments, but they require a lot of time to train in the early stage. This method is not universally applicable to workplaces such as substations that require real-time detection and real-time warning. In summary, the above research is aimed at a single instrument problem and does not involve the problems of uneven illumination, complex background and damping fluid interference at the same time. Pointer instrument detection is mainly divided into the following three steps: instrument detection, scale segment positioning and pointer fitting. When performing these steps, it will be affected by the light, the complicated background of the dial and the damping fluid. In order to solve the above problems, this paper proposes an automatic reading algorithm for dial gauges based on coordinate positioning. In general, the automatic reading algorithm based on coordinate positioning comprises two parts. One part is the coordinate extraction. This mainly includes the coordinate of the center and pointer of the dial. The other is the automatic recognition of the reading. The final reading result is obtained according to the relative position of the point on the dial. The automatic reading algorithm proposed in this paper falls into the following four steps:  
1. The enhancement of the image: The image preprocess step by using the MSRCR algorithm to reduce the influence from the brightness. 2.