Pizza Store Hygiene Monitoring System

Designed a real-time computer vision system to solve hygiene violations in pizza restaurants by detecting ingredient

handling without a scooper; annotated 1,000+ images to train a YOLOv12-Large model (94% mAP) and deployed

it via a socket-based pipeline with ROI tracking and timestamped violation logging.