Object Detection and Hazard Alert System for Child Safety on Robot using YOLO

This project outlines about Object Detection and Hazard Alert System for Child Safety on Robots Using YOLO. This invention integrated the YOLO (You Only Look Once) which helps in real time object detection ensuring child safety. By detecting the hazards and sending notification alerts, the system significantly reduced the hazardous situations providing safety to the children. The system is integrated with advanced machine learning and deep learning techniques for object detection that increases the accuracy and flexibility in automated systems and dynamic environments. This innovation ensures child safety, by enabling secured communication between the system and parents. Object Detection and Hazard Alert System for Child Safety Using YOLO is an application designed to ensure the child safety at home by continuously monitoring and detecting hazardous objects present near the child in real-time. The system is integrated with a camera placed at home to monitor the toddler movements and when child is present near a hazardous object the camera immediately detect the hazard and sends an instant alert to the parent’s mobile devices enabling immediate action and ensuring child safety. This application focus on leverages YOLO (You Only Look Once) for detecting the objects in single loop and OpenCV for real-time video processing. YOLO (You Only Look Once) incorporates robust and fast object detection ensuring identification of hazardous objects like knives, scissors, electrical wires, stair cases in a single loop. The project is executed in python with OpenCV integration that incorporates real-time video processing and hazard identification. This system demonstrates a significant improvement in detection accuracy and accurate response time compared to existing approaches. The project lays the foundation for the future advancements in child safety technologies by providing a robust, real-time solutions for hazard detection and prevention ensuring child is safe from hazard objects at home.

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Publication Details

Published
2025-03-06
DOI
https://doi.org/10.1109/icoeca66273.2025.00187
Primary Topic
Fire Detection and Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Object Detection and Hazard Alert System for Child Safety on Robot using YOLO

K Sivakrishna, Annapureddy Sreelekha, S. Sindhu, Gowrisankar Kalakoti et al.
Fire Detection and Safety Systems
article

Object Detection and Hazard Alert System for Child Safety on Robot using YOLO

K Sivakrishna, Annapureddy Sreelekha, S. Sindhu, Gowrisankar Kalakoti, J. Vaishnavi, Chappidi Varshitha
article en

Abstract

This project outlines about Object Detection and Hazard Alert System for Child Safety on Robots Using YOLO. This invention integrated the YOLO (You Only Look Once) which helps in real time object detection ensuring child safety. By detecting the hazards and sending notification alerts, the system significantly reduced the hazardous situations providing safety to the children. The system is integrated with advanced machine learning and deep learning techniques for object detection that increases the accuracy and flexibility in automated systems and dynamic environments. This innovation ensures child safety, by enabling secured communication between the system and parents. Object Detection and Hazard Alert System for Child Safety Using YOLO is an application designed to ensure the child safety at home by continuously monitoring and detecting hazardous objects present near the child in real-time. The system is integrated with a camera placed at home to monitor the toddler movements and when child is present near a hazardous object the camera immediately detect the hazard and sends an instant alert to the parent’s mobile devices enabling immediate action and ensuring child safety. This application focus on leverages YOLO (You Only Look Once) for detecting the objects in single loop and OpenCV for real-time video processing. YOLO (You Only Look Once) incorporates robust and fast object detection ensuring identification of hazardous objects like knives, scissors, electrical wires, stair cases in a single loop. The project is executed in python with OpenCV integration that incorporates real-time video processing and hazard identification. This system demonstrates a significant improvement in detection accuracy and accurate response time compared to existing approaches. The project lays the foundation for the future advancements in child safety technologies by providing a robust, real-time solutions for hazard detection and prevention ensuring child is safe from hazard objects at home.

Machine Intelligence Research Institute (US), Artificial Intelligence in Medicine (Canada) (CA)
Openalex Percentile: Top 75%
Fire Detection and Safety Systems
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