Object Detection and Hazard Alert System for Child Safety on Robot using YOLO
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".