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		<identifier>8JMKD3MGPEW34M/47LTDJ5</identifier>
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		<doi>10.1109/SIBGRAPI55357.2022.9991806</doi>
		<citationkey>OliveiraCaCaSoCâQu:2022:PuDaFa</citationkey>
		<title>PTL-AI Furnas Dataset: A Public Dataset for Fault Detection in Power Transmission Lines Using Aerial Images</title>
		<shorttitle>PTL-AI Furnas Dataset: A Public Dataset for Fault Detection in Power Transmission Lines Using Aerial Images</shorttitle>
		<format>On-line</format>
		<year>2022</year>
		<numberoffiles>1</numberoffiles>
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		<author>Oliveira, Frederico Santos de,</author>
		<author>Carvalho, Marcelo de,</author>
		<author>Campos, Pedro Henrique Tancredo,</author>
		<author>Soares, Anderson da Silva,</author>
		<author>Cândido Júnior, Arnaldo,</author>
		<author>Quirino, Ana Cláudia Rodrigues da Silva,</author>
		<affiliation>Universidade Federal de Mato Grosso (UFMT)</affiliation>
		<affiliation>Eletrobras-Furnas</affiliation>
		<affiliation>Eletrobras-Furnas</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<affiliation>Eletrobras-Furnas</affiliation>
		<e-mailaddress>fred_s0@yahoo.com.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 35 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal, RN</conferencelocation>
		<date>24-27 Oct. 2022</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>object detection, power transmission lines, fault detection.</keywords>
		<abstract>We present a new images dataset called PTL-AI Furnas Dataset as a new benchmark for fault detection in power transmission lines. This dataset has 6,295 images, with resolution 1280×720, extracted from the maintenance process of the energy transmission lines at Furnas company. It contains annotations of 17,808 components classified as baliser, bird nest, insulator, spacer and stockbridge. Furnas is a company that generates or transmits electricity to 51% of households in Brazil and more than 40% of the nations electricity passes through their grid enabling generating the dataset in different backgrounds and climatic conditions. We performed experiments using data augmentation techniques to train Faster R-CNN, Single-Shot Detects (SSD) and YoloV5 models. The benchmark result was obtained using the metrics of Mean Average Precision (mAP) and the Mean Average Recall (mAR) with values mAP=91.9% and mAR=89.7%. The PTL-AI Furnas Dataset is publicly available at https://github.com/freds0/PTL-AI Furnas Dataset.</abstract>
		<language>en</language>
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