{"id":396,"date":"2018-08-21T11:11:53","date_gmt":"2018-08-21T14:11:53","guid":{"rendered":"http:\/\/web.inf.ufpr.br\/vri\/?page_id=396"},"modified":"2022-01-11T01:30:56","modified_gmt":"2022-01-11T04:30:56","slug":"ufpr-amr","status":"publish","type":"page","link":"https:\/\/web.inf.ufpr.br\/vri\/databases\/ufpr-amr\/","title":{"rendered":"UFPR-AMR Dataset"},"content":{"rendered":"<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-large wp-image-401\" src=\"http:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample-1024x538.png\" alt=\"\" width=\"640\" height=\"336\" srcset=\"https:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample-1024x538.png 1024w, https:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample-300x158.png 300w, https:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample-768x404.png 768w, https:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample-360x189.png 360w, https:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2018\/08\/amr_sample.png 1497w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/><br \/>\nThis dataset, called UFPR-AMR dataset, contains 2,000 images taken from inside a warehouse of the Energy Company of Paran\u00e1 (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paran\u00e1. It has been introduced in our JEI paper [<a href=\"http:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2019\/08\/laroca2019convolutional.pdf\" target=\"_blank\" rel=\"noopener noreferrer\"><strong>PDF<\/strong><\/a>].<\/p>\n<p>The images were acquired with three different cameras and are available in the JPG format with a resolution between 2,340 \u00d7 4,160 and 3,120 \u00d7 4,160 pixels. The cameras used were: LG G3 D855, Samsung Galaxy J7 Prime and iPhone 6s.<\/p>\n<p>The dataset is split into three sets: training (800 images), validation (400 images) and testing (800 images).<\/p>\n<p>Every image has the following annotations available in a text file: the camera in which the image was taken, the counter\u2019s 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 <strong><a href=\"http:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2019\/08\/laroca2019convolutional.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">paper<\/a><\/strong>.<\/p>\n<h3>How to obtain the Dataset<\/h3>\n<p>The UFPR-AMR dataset is released for academic research only and is free to researchers from educational or research institutes for <strong>non-commercial purposes<\/strong>.<\/p>\n<p>Please click <strong><a href=\"http:\/\/web.inf.ufpr.br\/vri\/databases\/ufpr-amr\/license-agreement\/\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a><\/strong> for more info about obtaining the dataset.<\/p>\n<p>The annotations of the other public datasets used in our experiments are provided along with the UFPR-AMR dataset.<\/p>\n<p>You can now check who is downloading our dataset (see\u00a0<strong><a href=\"https:\/\/raysonlaroca.github.io\/misc\/ufpr-amr-map\/\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a><\/strong>).<\/p>\n<h3>References<\/h3>\n<p>If you use the UFPR-AMR dataset in your research, please cite our paper:<\/p>\n<ul>\n<li>R. Laroca, V. Barroso, M. A. Diniz, G. R. Gon\u00e7alves, W. R. Schwartz, D. Menotti, \u201cConvolutional Neural Networks for Automatic Meter Reading,\u201d Journal of Electronic Imaging, vol. 28, no. 1, p. 013023, 2019.\u00a0 [<strong><a href=\"https:\/\/web.inf.ufpr.br\/vri\/publications\/laroca2019convolutional\/\" target=\"_blank\" rel=\"noopener noreferrer\">Webpage<\/a><\/strong>] <b>[<a href=\"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-electronic-imaging\/volume-28\/issue-01\/013023\/Convolutional-neural-networks-for-automatic-meter-reading\/10.1117\/1.JEI.28.1.013023.full\" target=\"_blank\" rel=\"noopener noreferrer\">SPIE Digital Library<\/a>]<\/b>\u00a0<b>[<a href=\"http:\/\/web.inf.ufpr.br\/vri\/wp-content\/uploads\/sites\/7\/2019\/08\/laroca2019convolutional.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">PDF<\/a>] [<a href=\"https:\/\/raysonlaroca.github.io\/bibtex\/laroca2019convolutional.txt\" target=\"_blank\" rel=\"noopener noreferrer\">BibTeX<\/a>] [<strong><a href=\"http:\/\/www.inf.ufpr.br\/rblsantos\/misc\/copyright\/laroca2019convolutional.txt\" target=\"_blank\" rel=\"noreferrer noopener\">Copyright Notice<\/a><\/strong>]<\/b><\/li>\n<\/ul>\n<p class=\"indent\">You may also be interested in our\u00a0<span style=\"color: #ff0000\"><strong><span class=\"has-inline-color\">new research<\/span><\/strong><\/span>, where we proposed an end-to-end approach for AMR in unconstrained scenarios and introduced the\u00a0<a href=\"http:\/\/web.inf.ufpr.br\/vri\/databases\/copel-amr\/\" target=\"_blank\" rel=\"noreferrer noopener\">Copel-AMR<\/a>\u00a0dataset:<\/p>\n<ul>\n<li>R. Laroca, A. B. Araujo, L. A. Zanlorensi, E. C. de Almeida, D. Menotti, \u201cTowards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach,\u201d IEEE Access, vol. 9, pp. 67569-67584, 2021. [<a href=\"http:\/\/web.inf.ufpr.br\/vri\/publications\/amr-unconstrained-scenarios\/\" target=\"_blank\" rel=\"noreferrer noopener\">Webpage<\/a>] [<a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2021.3077415\" target=\"_blank\" rel=\"noreferrer noopener\">IEEE Xplore<\/a>] [<a href=\"https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=9422699\" target=\"_blank\" rel=\"noreferrer noopener\">PDF<\/a>] [<a href=\"https:\/\/raysonlaroca.github.io\/bibtex\/laroca2021towards.txt\" target=\"_blank\" rel=\"noreferrer noopener\">BibTeX<\/a>]<\/li>\n<\/ul>\n<h2>Related publications<\/h2>\n<p dir=\"auto\">A list of all papers on AMR published by us can be seen\u00a0<a href=\"https:\/\/scholar.google.com\/scholar?hl=pt-BR&amp;as_sdt=0%2C5&amp;as_ylo=2019&amp;q=allintitle%3A+meter+reading+author%3A%22David+Menotti%22&amp;btnG=\" target=\"_blank\" rel=\"noopener\">here<\/a>.<\/p>\n<h3>Contact<\/h3>\n<p>Please contact Rayson Laroca (<a href=\"mailto:rblsantos@inf.ufpr.br\" target=\"_blank\" rel=\"noopener noreferrer\">rblsantos@inf.ufpr.br<\/a>) with questions or comments.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This dataset, called UFPR-AMR dataset, contains 2,000 images taken from inside a warehouse of the Energy Company of Paran\u00e1 (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paran\u00e1. It has been introduced in <a href=\"https:\/\/web.inf.ufpr.br\/vri\/databases\/ufpr-amr\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":16,"featured_media":0,"parent":16,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-396","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/pages\/396","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/comments?post=396"}],"version-history":[{"count":30,"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/pages\/396\/revisions"}],"predecessor-version":[{"id":1965,"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/pages\/396\/revisions\/1965"}],"up":[{"embeddable":true,"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/pages\/16"}],"wp:attachment":[{"href":"https:\/\/web.inf.ufpr.br\/vri\/wp-json\/wp\/v2\/media?parent=396"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}