Convolutional neural networks for automatic meter reading

Laroca, Rayson; Barroso, Victor; Diniz, Matheus Alves; Gonçalves, Gabriel Resende; Schwartz, William Robson; Menotti, David

doi:10.1117/1.jei.28.1.013023

Cited by 65 publications

(105 citation statements)

References 36 publications

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“…This model was chosen for two main reasons. First, it was capable of detecting and recognizing LP characters at 448 FPS in [6] Second, very recently, it yielded the best recognition results in the context of image-based automatic meter reading [50], outperforming two segmentation-free approaches based on deep learning.…”

Section: License Plate Recognitionmentioning

confidence: 99%

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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413

271

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In this paper, we present an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the stateof-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models with various modifications, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.8% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

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Section: License Plate Recognitionmentioning

confidence: 99%

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

Self Cite

413

271

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“…• [Laroca et al 2019b] (Qualis A1) 5,6 -The proposed approach, which addresses the limitations of the system presented in [Laroca et al 2018] to considerably improve both the execution time (from 28ms to 14ms) and the recognition results (e.g., from 64.89% to 90% in the UFPR-ALPR dataset), was submitted to IET Intelligent Transport Systems (provisionally accepted subject to major revisions). • [Laroca et al 2019a] (Qualis A2) -We designed a two-stage approach for imagebased Automatic Meter Reading (AMR) leveraging many concepts presented in our works on ALPR [Laroca et al 2018, Laroca et al 2019b. In this work, published in the Journal of Electronic Imaging, we reported detection and recognition results significantly better than those obtained in previous works, with the networks used by us being able to process impressive 185+ FPS on a high-end GPU.…”

Section: Publications and Impactmentioning

confidence: 96%

Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector

Laroca¹,

Menotti²

2020

Anais Do Concurso De Teses E Dissertações Da SBC (CTD-SBC 2020)

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Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR that has become very popular, having been downloaded more than 550 times by researchers from 76 different countries in the last year alone. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature.

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“…But the digit numbers they process are complete and static, which is not suitable for rolling digit meter reading in a watermeter. Laroca et al [ 18 ] employs the Fast-YOLO object detector for components detection and evaluates three different CNN-based approaches for components recognition. They regarded the reading of the rolling digit as the goal of future work and did not propose a solution.…”

Section: Related Workmentioning

confidence: 99%

Image-Based Automatic Watermeter Reading under Challenging Environments

Hong

Yiwei

Lin

et al. 2021

Sensors

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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.

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Convolutional neural networks for automatic meter reading

Cited by 65 publications

References 36 publications

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector

Image-Based Automatic Watermeter Reading under Challenging Environments

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