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UFPR-AMR Dataset
2D Box
许可协议: Research Only

Overview

This dataset, called UFPR-AMR dataset, contains 2,000 images taken from inside a warehouse of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paraná. It has been introduced in our JEI paper [PDF].

The images were acquired with three different cameras and are available in the JPG format with a resolution between 2,340 × 4,160 and 3,120 × 4,160 pixels. The cameras used were: LG G3 D855, Samsung Galaxy J7 Prime and iPhone 6s.

The dataset is split into three sets: training (800 images), validation (400 images) and testing (800 images).

Every image has the following annotations available in a text file: the camera in which the image was taken, the counter’s position (x,y,w,h) and reading, as well as the position of each digit. All counters of the dataset (regardless of meter type) have 5 digits, and thus 10,000 digits were manually annotated. The full details are in our paper:

R. Laroca, V. Barroso, M. A. Diniz, G. R. Gonçalves, W. R. Schwartz, D. Menotti, “Convolutional Neural Networks for Automatic Meter Reading,” Journal of Electronic Imaging, vol. 28, pp. 1-14, 2019.

数据概要
数据格式
image,
数据量
2K
文件大小
--
发布方
Rayson Laroca
| 数据量 2K | 大小 --
UFPR-AMR Dataset
2D Box
许可协议: Research Only

Overview

This dataset, called UFPR-AMR dataset, contains 2,000 images taken from inside a warehouse of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paraná. It has been introduced in our JEI paper [PDF].

The images were acquired with three different cameras and are available in the JPG format with a resolution between 2,340 × 4,160 and 3,120 × 4,160 pixels. The cameras used were: LG G3 D855, Samsung Galaxy J7 Prime and iPhone 6s.

The dataset is split into three sets: training (800 images), validation (400 images) and testing (800 images).

Every image has the following annotations available in a text file: the camera in which the image was taken, the counter’s position (x,y,w,h) and reading, as well as the position of each digit. All counters of the dataset (regardless of meter type) have 5 digits, and thus 10,000 digits were manually annotated. The full details are in our paper:

R. Laroca, V. Barroso, M. A. Diniz, G. R. Gonçalves, W. R. Schwartz, D. Menotti, “Convolutional Neural Networks for Automatic Meter Reading,” Journal of Electronic Imaging, vol. 28, pp. 1-14, 2019.

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