Design and Implementation of Theft Detection Using YOLO Based Object Detection Methodology and Gen AI for Enhanced Security Solutions

This project introduces a intelligent theft detection system that utilizes the YOLOv8 (You Only Look Once v8) object detection model and generative AI along with real-time video surveillance and sound alarm functionality. The system constantly monitors video streams, identifies human occupancy in fenced or outofbounds zones, and sends immediate alerts, improving security and quick response. YOLOv8's advanced realtime processing abilities guarantee high-speed and accurate detection without sacrificing efficiency and thereby are the most suited for uses like survey line monitoring and surveillance. While in contrast to more conventional models like Faster R-CNN and SSD, which tend to sacrifice speed for accuracy or accuracy for speed, YOLOv8 manages to achieve the best of both. The system is implemented on the Streamlit platform, which offers an easy-to-use interface through which users can upload video files, modify confidence thresholds, and observe annotated results in real time. An in-built audio alert system implemented with Pygame also increases real-time responsiveness by alerting users to potential security threats. Through the integration of cutting-edge object detection with user-friendly and interactive experience, the solution provides a very effective and pragmatic solution for preventing theft with timely intervention as well as a security risk minimization. In addition, the system can be customized for multiple security purposes such as industrial monitoring, retail loss protection, and perimeter protection. Its efficiency and scalability provide it a solid solution for both large-scale and small-scale monitoring systems.