An Automatic Pointer Meter Reading Method based on Deep Learning in Gas Gathering Station

Abstract

Abstract pointer meters are widely used in gas gathering stations, and manual meter reading is time-consuming and laborious. This paper proposes an automatic reading method of pointer meters in gas gathering stations based on deep learning and image processing algorithm. Firstly, the input image is detected by YOLO neural network algorithm to quickly locate the position of the pointer meter in the image; then, the meter image is segmented from the background image, and after filtering and binarization preprocessing, the meter border is preliminarily detected by Hough transform. After further removing the background interference, the pointer area and scale value area are segmented by connected domain detection to detect. After measuring the center of mass of the scale value area, the center of the dial is corrected again by RANSAC fitting circle, and the pointer angle is identified by index table thinning algorithm, and finally the meter indication is calculated. The results show that this method can quickly locate the pointer meters in the gas gathering station under complex background and reduce the light interference.