Image Processing for Automatic Reading of Electro-Mechanical Utility Meters

Ocampo-Vega, Ricardo; Sánchez-Ante, Gildardo; Falcón-Morales, Luis E.; Sossa, Humberto

doi:10.1109/micai.2013.28

Cited by 14 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], the authors also employed handcrafted features for dial recognition. In addition to binarization and line intersection, the counter region was detected using Scale-Invariant Feature Transform (SIFT) features.…”

Section: B Dial Meter Readingmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

Salomon,

Laroca,

Menotti

2020

Preprint

View full text Add to dashboard Cite

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paraná (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multidial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).

show abstract

Section: B Dial Meter Readingmentioning

confidence: 99%

“…), dirt in the region of interest, and taken at a distance. In addition, most approaches are based on handcrafted features [11], [15], and were evaluated exclusively on private datasets [10], [11], [14]- [16]. To the best of our knowledge, there are no public datasets containing dial meter images in the literature.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

Salomon,

Laroca,

Menotti

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Ocampo-Vega, et al [4] introduce a methodology based on image processing and segmentation to enable the image acquisition and processing of pointer dials type energy meter to obtain efficient and accurate meter readings.…”

Section: Fig-2 Flowchart Of the Text Recognition In Ammeter Dial Platementioning

confidence: 99%

Untitled

2018

Review of Computer Engineering Research

View full text Add to dashboard Cite

The ability to use computer and human based interaction would make things easier for the user, but would be challenging for the researchers. More sophisticated imaging systems can handle inter plot the results of image analysis and describe the various objects and their connections in the scene. The electricity, gas, and water metering instruments may employ the Automatic meter reading (AMR) system for automation of bill generation process. The image processing implementation may ease the process of AMR. In this paper, we discussed the image processing applications in AMR. Contribution/Originality:This study is one of very few studies which have investigated the applications of image processing in automatic meter reading.

show abstract

“…In Reference [4], the image processing procedure is divided into four steps including image preprocessing, target region positioning, character segmentation and character recognition. Only for this last step, the authors use a neural network.…”

Section: Introductionmentioning

confidence: 99%

A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

Alexeev¹,

Kukharev

Matveev

et al. 2020

Mathematics

View full text Add to dashboard Cite

We investigate a neural network–based solution for the Automatic Meter Reading detection problem, applied to analog dial gauges. We employ a convolutional neural network with a non-linear Network in Network kernel. Presently, there is a significant interest in systems for automatic detection of analog dial gauges, particularly in the energy and household sectors, but the problem is not yet sufficiently addressed in research. Our method is a universal three-level model that takes an image as an input and outputs circular bounding areas, object classes, grids of reference points for all symbols on the front panel of the device and positions of display pointers. Since all analog pointer meters have a common nature, this multi-cascade model can serve various types of devices if its capacity is sufficient. The model is using global regression for locations of symbols, which provides resilient results even for low image quality and overlapping symbols. In this work, we do not focus on the pointer location detection since it heavily depends on the shape of the pointer. We prepare training data and benchmark the algorithm with our own framework a3net, not relying on third-party neural network solutions. The experimental results demonstrate the versatility of the proposed methods, high accuracy, and resilience of reference points detection.

show abstract

Image Processing for Automatic Reading of Electro-Mechanical Utility Meters

Cited by 14 publications

References 8 publications

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

Untitled

A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

Contact Info

Product

Resources

About