| Title: | GS3-1 Online Classroom Student Engagement Analysis using Enhanced YOLOv5 |
|---|---|
| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 974-978 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.GS3-1 |
| Author(s): | Shuai Wang, Abdul Shibghatullah, Javid Iqbal, Kay Hooi Keoy |
| Publication Date: | February 22, 2024 |
| Keywords: | Cyberbullying Mitigation, Emotional Analysis, Facial Expression Recognition, Online Classroom |
| Abstract: | The surge in online education has underscored the pressing issue of cyberbullying in virtual classrooms. This paper introduces an inventive method for early cyberbullying detection by analyzing students' engagement and emotional responses in online classrooms. The proposed SFER-YOLOv5 model, integrating Student Facial Expression Recognition with an enhanced YOLOv5 object detection model, incorporates transformative optimizations. These include Soft NMS for Non-Maximum Suppression, the integration of a Channel Attention (CA) module within the YOLOv5 architecture, and the use of Enhanced Intersection over Union (EIOU) as the bounding box regression loss function. This approach identifies diminished engagement and emotional irregularities, offering a proactive framework for mitigating cyberbullying in online classrooms. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/GS/GS3-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
(c)2008 Copyright The Regents of ALife Robotics Corporation Ltd. All Rights Reserved.