A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

Abstract

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

Real-time analogue gauge transcription on mobile phone

“…The system can reliably detect gauges in images, account for perspective distortion and accurately recover pointer position. Unlike previous attempts [5,14,15,21], our system is far more general and is capable of transcribing any circular gauge with one pointer. Our method can be used in industrial environments (on mobile phone or other embedded systems) to aid other systems such as robotic machine control, enable legacy industrial equipment to be IOT compatible or for public use as an app to easily record values from analogue meters.…”

“…The task of analogue gauge transcription has been tackled numerous times. Recent methods fall largely into two groups: those which use traditional based computer vision [3,20,25,8,18,23,9,26,17] and those which utilise deep learning [14,10,11,21,1,16,5,15]. Methods using traditional based approaches are typically brittle to appearance variation in lighting, background clutter and highly constrained to particular types of gauges.…”

“…While many prior works are still using the traditional methods, there is a growing trend in applying deep learning to the task [10,14,15,16]. However, these works often incorporate deep learning in just part of their transcription pipeline, probably due to the severe lack of suitable train-ing data.…”

Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

“…High quality meter images can facilitate the extraction of the basic information needed for the reading recognition process, and image quality enhancement is necessary after obtaining the meter images. Image processing-based methods for meter image quality enhancement include methods such as image greyscaling to reduce computational effort [30], threshold segmentation to obtain target regions [31], and histogram equalisation to improve image contrast [32]. For the difficulty in obtaining real datasets, researchers have generated virtual meteration data in large quantities by means of deep learning [33,34].…”

Coordinates transformation method for pointer gauge reading by machine vision

“…Noises can drastically reduce the performance of the system. Some complicated methods, like neural network algorithms, are needed to maintain the accuracy of the machine reading [8][9][10].…”