KidShield - Hazard Detection

This project presents a real-time home hazard detection and alert system using computer vision and deep learning. YOLOv3 is employed to identify dangerous objects like knives and guns in a live video feed. When a hazard is detected near a person, the system triggers visual overlays and audio alerts using text-to-speech. It processes frames continuously, drawing labeled bounding boxes to distinguish threats from safe objects. Pre-trained YOLOv3 weights and COCO dataset classes are utilized for accurate detection. This approach improves home safety by enabling continuous, automated monitoring and minimizing human error.