{"created":"2025-01-19T01:44:59.186686+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240663","sets":["1164:2822:11469:11788"]},"path":["11788"],"owner":"44499","recid":"240663","title":["Self-Attentionを用いた3次元物体検出手法の高精度化と高速化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-13"},"_buckets":{"deposit":"4465fde2-a07b-4022-a999-5720fa59ec03"},"_deposit":{"id":"240663","pid":{"type":"depid","value":"240663","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Self-Attentionを用いた3次元物体検出手法の高精度化と高速化","author_link":["660692","660694","660693","660691"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Self-Attentionを用いた3次元物体検出手法の高精度化と高速化"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"リアルタイム・高速化","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-11-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学"},{"subitem_text_value":"大阪大学"},{"subitem_text_value":"富山県立大学"},{"subitem_text_value":"立命館大学"}]},"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/240663/files/IPSJ-EMB24067005.pdf","label":"IPSJ-EMB24067005.pdf"},"date":[{"dateType":"Available","dateValue":"2026-11-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-EMB24067005.pdf","filesize":[{"value":"441.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"42"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9f922f26-825e-44cc-834e-6057e0569daf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"坂井, 優斗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西川, 広記"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"孔, 祥博"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"冨山, 宏之"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12149313","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":"2188-868X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"3 次元物体検出は，自動運転車が周囲の環境を認識するための重要なタスクであり，高速かつ高精度な検出が求められる．本論文では，LiDAR 点群と RGB 画像を用いた既存手法を分析し，Self-Attention を組み込むことで，高精度化と高速化を図った．実験の結果，提案手法は従来手法と比較して処理速度を 5.03 倍向上させ，検出精度も 2.61 %向上することを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2","bibliographic_titles":[{"bibliographic_title":"研究報告組込みシステム（EMB）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2024-EMB-67"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":240663,"updated":"2025-01-19T07:54:19.774449+00:00","links":{}}