Work

AI-Powered Detection of Forgotten Children in Vehicles Using YOLOv11 for Enhanced Safety

Year: 2025

Type: conference-paper

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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.os13-2

Authors: Nur Atikah Jefri, Sarah Atifah Saruchi, Radhiyah Abd Aziz, Aqil Hafizzan Nordin, Ammar A Al-Talib, Zulhaidi Mohd Jawi

Institutions: Universiti Malaysia Pahang Al-Sultan Abdullah, UCSI University, Institut Penyelidikan Keselamatan Jalan Raya Malaysia

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Universiti Malaysia Pahang

OpenAlex ID: W4409913742