{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00183573","sets":["1164:2735:9079:9248"]},"path":["9248"],"owner":"11","recid":"183573","title":["Fully Convolutional Networkを用いたインフラ点検におけるひび割れの自動検出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-09-18"},"_buckets":{"deposit":"d1fdc498-dd81-4087-96da-175577aa6342"},"_deposit":{"id":"183573","pid":{"type":"depid","value":"183573","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Fully Convolutional Networkを用いたインフラ点検におけるひび割れの自動検出","author_link":["403352","403354","403351","403356","403355","403353"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Fully Convolutional Networkを用いたインフラ点検におけるひび割れの自動検出"},{"subitem_title":"Automatic Crack Detection in Infrastructure Inspection Using Fully Convolutional Network","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2017-09-18","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大学大学院システム情報工学研究科コンピュータサイエンス専攻"},{"subitem_text_value":"筑波大学システム情報系情報工学域"},{"subitem_text_value":"筑波大学システム情報系情報工学域"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/183573/files/IPSJ-MPS17115002.pdf","label":"IPSJ-MPS17115002.pdf"},"date":[{"dateType":"Available","dateValue":"2019-09-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS17115002.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"33a79162-fe18-4e52-a375-38480644a9af","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"木村, 宇任"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"アランニャ, クラウス"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"櫻井, 鉄也"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takato, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Aranha, Claus","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetsuya, Sakurai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"インフラの老朽化に伴うひび割れは重大な事故の要因となるため，継続的な点検によってひび割れを発見し適切な修繕を行う必要がある．現在，主に人による目視検査や打音検査が行われているが，時間や人員コストがかかるため，画像解析によるひび割れの自動検出が期待されている．ひび割れの自動検出に関する従来手法の課題として，検出精度が十分でないことや，計算時間がかかるといったことが挙げられる．このような課題に対して本論文では，画像の領域分割問題において高い性能を示している Fully Convolutional Network を用いた手法を提案する．ひび割れ画像のデータセットを用いた評価実験によって，提案手法が従来手法より優れていることを示した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"It is necessary to detect cracks of infrastructure caused by aging by periodic inspection and make appropriate repairs, since they become a factor of serious accidents. Currently, manual visual inspection and hammering tests are mainly used to detect cracks, but it takes time and labor cost. For that reason, automatic detection of cracks by image analysis is necessary. Conventional methods of automatic crack detection have problems with detection accuracy and computation cost. We propose a novel crack detection method using Fully Convolutional Network which shows high performance in semantic segmentation of image. We show that the proposed method is superior to conventional methods by evaluation experiments using dataset of crack images.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2017-09-18","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2017-MPS-115"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":183573,"updated":"2025-01-20T03:36:49.251212+00:00","links":{},"created":"2025-01-19T00:51:05.043217+00:00"}