{"created":"2025-01-19T01:21:12.310923+00:00","updated":"2025-01-19T14:15:39.949356+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00221194","sets":["6504:11035:11042"]},"path":["11042"],"owner":"44499","recid":"221194","title":["AlexNetを用いた交通事故種別の分類における地図データセットのズームレベルの評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-17"},"_buckets":{"deposit":"7a496a9a-176d-43ca-b3f9-de25c086ad49"},"_deposit":{"id":"221194","pid":{"type":"depid","value":"221194","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"AlexNetを用いた交通事故種別の分類における地図データセットのズームレベルの評価","author_link":["578966","578965","578967","578969","578968"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"AlexNetを用いた交通事故種別の分類における地図データセットのズームレベルの評価"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2022-02-17","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":"椙山女学園大"},{"subitem_text_value":"愛知工大"},{"subitem_text_value":"愛知大"},{"subitem_text_value":"愛知大"},{"subitem_text_value":"愛知工大"}]},"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/221194/files/IPSJ-Z84-2E-04.pdf","label":"IPSJ-Z84-2E-04.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-22"}],"format":"application/pdf","filename":"IPSJ-Z84-2E-04.pdf","filesize":[{"value":"525.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"d003a501-a3e4-4307-8838-d001f2c8c020","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"向, 直人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"内種, 岳詞"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岩田, 員典"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"蒋, 湧"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"伊藤, 暢浩"}],"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":"愛知県警察から提供を受けた過去の交通事故のデータを利用し，畳み込みニューラルネットワークの学習用のデータセットを構築する．データセットは，事故発生地点の緯度・経度を基に生成されたズームレベル17の道路地図画像（1辺は約117m）で構成される．これまでに，我々は，このデータセットを用いることで，追突・出会頭・右左折時の事故種別を約70%の正解率で分類できることを示した．しかし，ズームレベルが正解率に与える影響は検討していない．そこで，本研究では，ズームレベル17に加えて，16（1辺が約234m）と18（1辺が約59m）のデータセットを構築し，それらを比較することで，事故の要因となる道路の構造や範囲を明らかにする．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"40","bibliographic_titles":[{"bibliographic_title":"第84回全国大会講演論文集"}],"bibliographicPageStart":"39","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":221194,"links":{}}