Image-Based Automatic Watermeter Reading under Challenging Environments

Abstract

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception

“…However, the literature review indicates that, generally, deep neural networks achieve superior performance in both accuracy and robustness (e.g., change in lightning conditions, contrast, perspective); therefore, they are chosen as state-of-the-art approaches [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Convolutional neural networks represent de facto standard in the field of image processing and are chosen as an image processing model.…”

“…Digitalization of analog instruments with gauge using vision-based systems has been demonstrated successfully many times since the vast majority of legacy instruments have gauge, such as aircraft instruments [ 4 ], water meter [ 5 ], energy meters [ 6 ], voltmeters [ 7 ], pressure gauges [ 8 ] and ammeters [ 9 ], power meters [ 10 ] etc.…”

Image-Based Automatic Energy Meter Reading Using Deep Learning

“…The methods used in this paper include a Mask-RCNN for counter detection, digit segmentation and digit recognition using the a dataset consisting of 2,000 fully annotated images and achieves correct recognition rates of 100%. An image-based meter reading for water meters is proposed in [18] where the robustness to changes in orientation and spatial layout is achieved by enriching the layers of a neural network with necessary information. Image-based meter reading is performed in unconstrained environment [19] via a combination of YOLO-v4 and the so-called (AngReg) regression approach where the meter recognition is 98.90%.…”