{"id":205384,"created":"2025-01-19T01:07:32.460162+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205384","sets":["6504:10247:10254"]},"path":["10254"],"owner":"6748","recid":"205384","title":["Faster R-CNNを用いた路面のひび割れ特定とカテゴリー推定モデルの構築"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"f1b86eec-6e2a-478c-8451-27a4c28ed564"},"_deposit":{"id":"205384","pid":{"type":"depid","value":"205384","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"Faster R-CNNを用いた路面のひび割れ特定とカテゴリー推定モデルの構築","author_link":["509586"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Faster R-CNNを用いた路面のひび割れ特定とカテゴリー推定モデルの構築"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2020-02-20","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岩手県大"}]},"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/205384/files/IPSJ-Z82-2U-03.pdf","label":"IPSJ-Z82-2U-03.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-2U-03.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"172ab3fd-2abd-42de-a9a3-9f390bf7187a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大竹, 主真"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"　現在日本では高度経済成長期以降に整備された道路の老朽化が進んでいる．道路の老朽化の点検はコストのかかる路面性状測定車や，目視による点検を行っているため，低コストかつ効率の良い点検方法が必要である．本稿ではFaster R-CNNを用いた路面のひび割れの検出とカテゴリー推定のモデルを構築する．Faster R-CNNの学習ネットワークには事前学習済みネットワークResNet-50を利用し，転移学習を行う．また，ひび割れの検出をする際に使用するアンカーボックスのサイズを学習データセットのひび割れのサイズの統計から自動的に決定することによってアンカーボックスの最適化を効率よく行っている．以上により，低コストで効率的なひび割れの検出を目指す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"538","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"537","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"updated":"2025-01-19T19:56:30.377428+00:00","links":{}}