{"created":"2024-12-12T09:34:50.837515+00:00","id":2010958,"links":{},"metadata":{"_buckets":{"deposit":"62dc9f7a-f3c7-44f8-98fd-80ef11cff7f1"},"_deposit":{"created_by":7,"id":"2010958","owners":[7],"pid":{"revision_id":0,"type":"depid","value":"2010958"},"status":"published"},"_oai":{"id":"oai:tokushima-u.repo.nii.ac.jp:02010958","sets":["1713853213384:1713853295607"]},"author_link":["401","769"],"control_number":"2010958","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-10-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicPageStart":"1809","bibliographicVolumeNumber":"12","bibliographic_titles":[{"bibliographic_title":"Metals","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MDPI","subitem_publisher_language":"en"}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).","subitem_rights_language":"en"}]},"item_10001_source_id_9":{"attribute_name":"収録物ID","attribute_value_mlt":[{"subitem_source_identifier":"20754701","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1715043197608":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open 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Xin","creatorNameLang":"en"}],"familyNames":[{"familyName":"康","familyNameLang":"ja"},{"familyName":"コウ","familyNameLang":"ja-Kana"},{"familyName":"Kang","familyNameLang":"en"}],"givenNames":[{"givenName":"鑫","givenNameLang":"ja"},{"givenName":"シン","givenNameLang":"ja-Kana"},{"givenName":"Xin","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"769","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"292960/profile-ja.html","nameIdentifierScheme":"徳島大学 教育研究者総覧","nameIdentifierURI":"http://pub2.db.tokushima-u.ac.jp/ERD/person/292960/profile-ja.html"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2023-04-25"}],"displaytype":"detail","filename":"met_12_11_1809.pdf","filesize":[{"value":"2.64 MB"}],"format":"application/pdf","licensetype":"license_0","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://tokushima-u.repo.nii.ac.jp/record/2010958/files/met_12_11_1809.pdf"},"version_id":"3f22e0d3-b22d-4a5b-8189-4c07fb10dc05"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"aluminum profile","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"surface defect classification","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"RepVGG","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"CBAM","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"attention mechanism","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"7","path":["1713853295607"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-04-25"},"publish_date":"2023-04-25","publish_status":"0","recid":"2010958","relation_version_is_last":true,"title":["An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles"],"weko_creator_id":"7","weko_shared_id":-1},"updated":"2025-02-13T05:58:25.847788+00:00"}