| Title: | OS13-2 AI-Powered Detection of Forgotten Children in Vehicles Using YOLOv11 for Enhanced Safety |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 387-391 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS13-2 |
| Author(s): | Nur Atikah Jefri, Sarah Atifah Saruchi, Radhiyah Abd Aziz, Aqil Hafizzan Nordin, Ammar A Al-Talib, Zulhaidi Mohd Jawi |
| Publication Date: | February 13, 2025 |
| Keywords: | AI child detection, unattended children, vehicle safety, YOLOv11, deep learning, CVAT, child presence detection, AI training |
| Abstract: | This study proposes a child presence detection system in vehicles, focusing on evaluating the performance of YOLOv11 for accurate detection and identification. To train the system, images simulating a child's presence in vehicles were collected using a doll, and these annotated images were labeled with the Computer Vision Annotation Tool (CVAT). The study emphasizes the potential of YOLOv11 as an effective and reliable solution for unattended child detection in vehicles. By leveraging advanced deep learning techniques, this research highlights the importance of addressing critical safety issues. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS13/OS13-2.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/ |
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