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  1. 学術雑誌論文
  2. 5 技術(工学)

YOLOv8s-NE: Enhancing Object Detection of Small Objects in Nursery Environments Based on Improved YOLOv8

http://hdl.handle.net/10228/0002001267
http://hdl.handle.net/10228/0002001267
cd4df106-d7d0-4c3b-8a68-4fa05f20bb74
名前 / ファイル ライセンス アクション
10445748.pdf 10445748.pdf (1.5 MB)
アイテムタイプ 共通アイテムタイプ(1)
公開日 2025-02-07
タイトル
タイトル YOLOv8s-NE: Enhancing Object Detection of Small Objects in Nursery Environments Based on Improved YOLOv8
言語 en
著者 Amir, Supri Bin

× Amir, Supri Bin

en Amir, Supri Bin
Amir, S.B.

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堀尾, 恵一

× 堀尾, 恵一

WEKO 6199
e-Rad_Researcher 70363413
Scopus著者ID 35561857100
九工大研究者情報 356

en Horio, Keiichi

ja 堀尾, 恵一

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著作権関連情報
権利情報 Copyright (c) 2024 by the authors. Licensee MDPI, Basel, Switzerland.
著作権関連情報
権利情報Resource https://creativecommons.org/licenses/by/4.0/
権利情報 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
抄録
内容記述タイプ Abstract
内容記述 The primary objective of this research investigation is to examine object detection within the specific environment of a nursery. The nursery environment presents a complex scene with a multitude of objects, varying in size and background. To simulate real-world conditions, we gathered data from a nursery. Our study is centered around the detection of small objects, particularly in nursery settings where objects that include stationery, toys, and small accessories are commonly present. These objects are of significant importance in facilitating cognition of the activities and interactions taking place within the confines of the room. Due to their small size and the possibility of occlusion by other objects or children, precisely detecting these objects is regrettably fraught with inherent challenges. This study introduces YOLOv8s-NE in an effort to enhance the detection of small objects found in the nursery. We improve the standard YOLOv8 by incorporating an extra detection head to effectively for small objects. We replace the C2f module with C2f_DCN to further improve the model’s ability to detect objects of varying sizes that can be deformed or occluded within the image. Furthermore, we introduce NAM attention to focus on the important features and ignore less informative ones, thereby improving the accuracy of our proposed model. We used the five-fold cross-validation approach to split the dataset in order to evaluate the performance of YOLOv8s-NE, thereby facilitating a more comprehensive model evaluation. Our model achieves 34.1% of APs, 45.1% of mAP50:90, and 76.7% of mAP50 detection accuracy at 37.55 FPS on the nursery dataset. In terms of APs, mAP50:90, and mAP50 metrics, our proposed YOLOv8s-NE model outperforms the standard YOLOv8s model, with improvements of 4.6%, 4.7%, and 3.9%, respectively. We apply our proposed YOLOv8s-NE model as a safety system by developing an algorithm to detect objects on top of cabinets that could be potentially risky to children.
言語 en
書誌情報 en : Electronics

巻 13, 号 16, p. 3293, 発行日 2024-08-19
出版社
出版者 MDPI
キーワード
主題Scheme Other
主題 small object detection
キーワード
主題Scheme Other
主題 multiple detection head
キーワード
主題Scheme Other
主題 DCNv2
キーワード
主題Scheme Other
主題 attention mechanism
キーワード
主題Scheme Other
主題 nursery
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
識別子タイプ DOI
関連識別子 https://doi.org/10.3390/electronics13163293
ISSN
収録物識別子タイプ EISSN
収録物識別子 2079-9292
研究者情報
URL https://hyokadb02.jimu.kyutech.ac.jp/html/356_ja.html
論文ID(連携)
値 10445748
連携ID
値 12905
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