@article{oai:doshisha.repo.nii.ac.jp:00027219,
 author = {米田, 浩崇 and Yoneda, Hirotaka and 槇原, 絵里奈 and Makihara, Erina and 馬場, 建 and Baba, Takeru and 前田, 侑哉 and Maeda, Yuya and 三木, 光範 and Miki, Mitsunori},
 issue = {1},
 journal = {同志社大学ハリス理化学研究報告, The Harris science review of Doshisha University},
 month = {Apr},
 note = {多数の要素を同時に考慮する必要がある組込みシステム開発において，熟練者が持つ暗黙的知識を初学者に伝えることで学習効率の向上が期待できる．本稿では，暗黙的知識が反映される視線情報に注目し，ウェアラブル型アイトラッカーを用いた注目物体検出システムを提案した．YOLO v3を用いた機械学習により注目物体を自動的に認識するシステムを構築し，組込みシステム開発におけるディスプレイ，問題用紙，Arduino，回路の4要素の識別を行った．提案システムの識別精度評価実験の結果，人間が目視で確認を行った注目物体とシステムが判定した注目物体は平均88%一致した．作業時間全体に占める各物体への注目時間や，物体間の注目遷移の比率など，情報を比として捉える必要があるとき，提案手法は作業者の注目物体をめぐる視線情報の解析に寄与できると考えられる．, In the embedded systems development that needed to consider many factors simultaneously, it is expected that the learning efficiency will be improved by passing experts' implicit tips on to beginners. In this paper, we focus on gaze information that reflects implicit tips and propose an attention object detection system using a wearable eye tracker. We constructed a system for automatically recognizing an object by machine learning using YOLO v3 and identified four elements in the embedded systems development; display, paper, Arduino and circuit. As the result of the experiment on the evaluation of the recognition accuracy, the target object checked by humans and the target object detected by the system matched 88% on average. When it comes to the information which we needs to focused on as a ratio, such as the attention time of each object in the entire developing time and the ratio of attention transitions between objects, our system should contribute to the analysis of the gaze information of a developer's attention object., application/pdf},
 pages = {36--40},
 title = {組込みシステム開発におけるYOLO v3を用いた注目物体検出手法の提案},
 volume = {61},
 year = {2020},
 yomi = {ヨネダ, ヒロタカ and マキハラ, エリナ and ババ, タケル and マエダ, ユウヤ and ミキ, ミツノリ}
}