エンドユーザ向けアボカド食べ頃分類モバイルアプリにおける深層距離学習を利用した分類手法の検討

• Item type: SIG Technical Reports(1)
• 公開日: 2021-05-25
• タイトル: エンドユーザ向けアボカド食べ頃分類モバイルアプリにおける深層距離学習を利用した分類手法の検討
• タイトル / 言語: en
• タイトル: Investigation of deep metric learning for mobile application that classifies avocado ripeness for end users.
• 言語: jpn
• キーワード / 主題Scheme: Other
• キーワード / 主題: 計測・認識・制御
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: 中京大学大学院工学研究科情報工学専攻
• 著者所属: 中京大学工学部情報工学科
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering,Chukyo University
• 著者所属(英): en
• 著者所属(英): School of Engineering,Chukyo University
• 著者名: 杉本, 隼斗; 濱川, 礼
• 著者名(英): Hayato, Sugimoto; Rei, Hamakawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: It is said that the eating time of an avocado can be determined using the color, texture, and firmness of the rind as indicators, but it is difficult to determine the eating time of an avocado with high accuracy only for experienced users, which is a problem faced by end users of avocados. In this study, we developed a deep learning model that classifies avocados into three classes (unripe, ripe, and overripe) based on image input, and implemented a mobile application that can be run on a smartphone equipped with the model. For the deep learning model, we investigated a classification method using deep metric learning. Deep metric learning has achieved many successes in face recognition tasks. It is considered useful when applying deep learning to datasets with small differences in image features between classes and a small amount of data for each class, and the dataset we collected in this study has the same characteristics. The model was able to classify the evaluation data with an accuracy of 89.77%.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: It is said that the eating time of an avocado can be determined using the color, texture, and firmness of the rind as indicators, but it is difficult to determine the eating time of an avocado with high accuracy only for experienced users, which is a problem faced by end users of avocados. In this study, we developed a deep learning model that classifies avocados into three classes (unripe, ripe, and overripe) based on image input, and implemented a mobile application that can be run on a smartphone equipped with the model. For the deep learning model, we investigated a classification method using deep metric learning. Deep metric learning has achieved many successes in face recognition tasks. It is considered useful when applying deep learning to datasets with small differences in image features between classes and a small amount of data for each class, and the dataset we collected in this study has the same characteristics. The model was able to classify the evaluation data with an accuracy of 89.77%.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12049625
• 書誌情報: 研究報告エンタテインメントコンピューティング（EC）; 巻 2021-EC-60, 号 5, p. 1-4, 発行日 2021-05-25
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8914
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会