<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://etreview.ru/lib/pkp/xml/oai2.xsl" ?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/"
	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/
		http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
	<responseDate>2026-08-11T00:45:02Z</responseDate>
	<request identifier="oai:etreview.ru:article/166" metadataPrefix="jats" verb="GetRecord">https://etreview.ru/index.php/et/oai</request>
	<GetRecord>
		<record>
			<header>
				<identifier>oai:etreview.ru:article/166</identifier>
				<datestamp>2025-09-12T17:00:25Z</datestamp>
				<setSpec>et:IAM</setSpec>
			</header>
			<metadata>
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://jats.nlm.nih.gov/publishing/1.1/" dtd-version="1.1" xsi:noNamespaceSchemaLocation="https://jats.nlm.nih.gov/archiving/1.4/xsd/JATS-archivearticle1.xsd" xml:lang="ru" specific-use="eps-0.1">
			<front>
			<journal-meta>
				<journal-id journal-id-type="publisher">et</journal-id><journal-id journal-id-type="ojs">et</journal-id>
				<journal-title-group>
			<journal-title xml:lang="ru">Вопросы природопользования</journal-title><trans-title-group xml:lang="en"><trans-title>Environmental Management Issues</trans-title></trans-title-group>
</journal-title-group>			<issn pub-type="epub">3034-3461</issn>			<publisher><publisher-name>Индивидуальный предприниматель Подколзин М.М.</publisher-name></publisher>
			<self-uri xlink:href="https://etreview.ru/index.php/et"/>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="doi">10.25726/i1111-6981-6065-r</article-id><article-id pub-id-type="publisher-id">166</article-id>
			<article-categories><subj-group subj-group-type="heading" xml:lang="en"><subject>INFORMATION AND MANAGEMENT</subject></subj-group><subj-group subj-group-type="heading" xml:lang="ru"><subject>ИНФОРМАЦИЯ И УПРАВЛЕНИЕ</subject></subj-group></article-categories>
			<title-group><article-title xml:lang="ru">Методы определения дефектов поверхности изделий из металлов и их сплавов с помощью методов машинного зрения</article-title><trans-title-group xml:lang="en"><trans-title>Methods for determining surface defects of products made of metals and their alloys using machine vision methods</trans-title></trans-title-group></title-group>
			<contrib-group content-type="author">
				<contrib contrib-type="author">
					<name-alternatives>
						<name name-style="western" specific-use="primary" xml:lang="ru">
							<surname>Чуфаров</surname>
							<given-names>Иван Валерьевич</given-names>
						</name>
						<name name-style="western" xml:lang="en">
							<surname>Chufarov</surname>
							<given-names>Ivan V.</given-names>
						</name>
					</name-alternatives>
					<xref ref-type="aff" rid="aff-1"/>
					<email>73ivan@mail.ru</email>
				</contrib>
			</contrib-group>
			<aff-alternatives id="aff-1">
				<aff xml:lang="ru"><institution content-type="orgname">Ульяновский государственный технический университет</institution></aff>
				<aff xml:lang="en"><institution content-type="orgname">Ulyanovsk State Technical University</institution></aff>
			</aff-alternatives>
			<pub-date date-type="collection"><year>2025</year></pub-date><pub-date date-type="pub" publication-format="epub"><day>15</day><month>01</month><year>2025</year></pub-date>
			<volume seq="2">44</volume>
			<issue>11</issue>
				<issue-id>12</issue-id><issue-title xml:lang="ru">Вопросы природопользования</issue-title><issue-title xml:lang="en">Environmental management issues</issue-title><fpage>61</fpage>
				<lpage>64</lpage>
			<history>
				<date date-type="received" iso-8601-date="2025-04-04">
					<day>04</day>
					<month>04</month>
					<year>2025</year>
				</date>
			</history>
			<permissions>
				<copyright-statement>Copyright (c) 2025 Вопросы природопользования</copyright-statement>
				<copyright-year>2025</copyright-year>
				<copyright-holder>Вопросы природопользования</copyright-holder>
				<license xml:lang="ru" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0">
					<license-p>Это произведение доступно по лицензии Creative Commons «Attribution-NonCommercial-NoDerivatives» («Атрибуция — Некоммерческое использование — Без производных произведений») 4.0 Всемирная.</license-p>
				</license>
				<license license-type="open-access" specific-use="metadata" xml:lang="ru" xlink:href="https://creativecommons.org/publicdomain/zero/1.0/">
					<license-p>Метаданные настоящей записи распространяются на условиях Creative Commons CC0 1.0 (передача в общественное достояние).</license-p>
				</license>
			</permissions>
			
			<self-uri xlink:href="https://etreview.ru/index.php/et/article/view/166"/>
			<abstract><p>Выполнен обзор актуальности задачи поиска дефектов поверхности изделий из металлов и их сплавов методами машинного зрения. Рассмотрены основные группы методов машинного зрения для поиска дефектов на поверхности изделий из металлов и их сплавов. Рассмотрены особенности поиска дефектов на поверхности изделий из металлов и сплавов по сравнению с другими поверхностями. Показаны трудности, с которыми можно столкнуться, применяя методы глубокого обучения при поиске дефектов на поверхности изделий из металлов и их сплавов и способы их обхода.</p></abstract><trans-abstract xml:lang="en"><p>The review of the relevance of the problem of searching for surface defects of products made of metals and their alloys by machine vision methods is performed. The main groups of machine vision methods for searching for defects on the surface of metal products and their alloys are considered. The features of the search for defects on the surface of products made of metals and alloys in comparison with other surfaces are considered. The difficulties that can be encountered using deep learning methods when searching for defects on the surface of products made of metals and their alloys and ways to circumvent them are shown.</p></trans-abstract><trans-abstract xml:lang="en"><p>The review of the relevance of the problem of searching for surface defects of products made of metals and their alloys by machine vision methods is performed. The main groups of machine vision methods for searching for defects on the surface of metal products and their alloys are considered. The features of the search for defects on the surface of products made of metals and alloys in comparison with other surfaces are considered. The difficulties that can be encountered using deep learning methods when searching for defects on the surface of products made of metals and their alloys and ways to circumvent them are shown.</p></trans-abstract>
			
			
			<kwd-group xml:lang="ru"><title>Ключевые слова</title><kwd>машинное зрение</kwd><kwd>дефекты поверхности</kwd><kwd>металлы и их сплавы</kwd></kwd-group><kwd-group xml:lang="en"><title>Keywords</title><kwd>machine vision</kwd><kwd>surface defects</kwd><kwd>metals and their alloys</kwd></kwd-group><funding-group>
				<funding-statement xml:lang="ru">Исследование выполнено без внешнего финансирования.</funding-statement>
				<funding-statement xml:lang="en">The study was conducted without external funding.</funding-statement>
			</funding-group>
			<counts><page-count count="4"/></counts>
			<custom-meta-group><custom-meta><meta-name>issue-cover</meta-name><meta-value><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://etreview.ru/public/journals/1/cover_issue_12_ru_RU.jpg"/></meta-value></custom-meta></custom-meta-group><custom-meta-group>
				<custom-meta>
					<meta-name>metadata-license</meta-name>
					<meta-value><ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</ext-link></meta-value>
				</custom-meta>
			</custom-meta-group>
		</article-meta>
	</front>
	<back>
		<ref-list xml:lang="ru">
			<title>Список литературы</title>
			<ref id="R1"><mixed-citation>Андриянов Н.А., Дементьев В.Е., Ташлинский А.Г. Обнаружение объектов на изображении: от критериев Байеса и Неймана–Пирсона к детекторам на базе нейронных сетей EfficientDet // Компьютерная оптика. 2022. Т. 46. № 1. С. 139-159.</mixed-citation></ref>
			<ref id="R2"><mixed-citation>Дементьев В.Е., Савинов Р.А., Суетин М.Н., Подлобошников А.Г. Система распознавания повреждений металлических конструкций // Автоматизация процессов управления. 2021. №2(2021). С. 40-45.</mixed-citation></ref>
			<ref id="R3"><mixed-citation>Chuande Zh., Zhenyu L., Zhongliang L., Minghui M., Yonghu T., Kewen X., Kang L., Haliun Z. Metal surface defect detection based on improved YOLOv5 // Scientific reports. 2023. https://www.nature.com/articles/s41598-023-47716-2</mixed-citation></ref>
			<ref id="R4"><mixed-citation>Chen Ya., Ding Yu., Zhao F., Zhang E., Wu Zh., Shao L. Surface defect detection methods for industrial products: a review // Applied science. MDPI. 2021. https://www.mdpi.com/2076-3417/11/16/7657</mixed-citation></ref>
		</ref-list>
	</back>
</article>			</metadata>
		</record>
	</GetRecord>
</OAI-PMH>
