Effective monitoring has become essential part in the modern day to ensure public safety, particularly in congested areas such as malls, schools, hospitals, and public transit hubs. The continuous human monitoring is a major component of traditional CCTV-based security systems is prone to human mistake, weariness, and delayed emergency response times. In order to detect potentially hazardous things, such knives or firearms, straight from live camera feeds, this study proposes an AI-powered real-time object identification system that makes use of the YOLOv5 (You Only Look Once version 5) deep learning model.The system is designed to recognize threats with high accuracy and speed. Upon detection of a suspicious object, it immediately triggers an automated alert through visual and audio signals to notify the relevant security personnel. This prompt response can help prevent escalations and allow faster action in critical situations. The model is trained on a custom dataset consisting of various object classes, with a focus on hazardous items. The entire process, from capturing live video to object detection and notification, is done in real-time with minimal latency.The solution combines front-end interfaces for monitoring and alerting with a robust back-end infrastructure that handles video streaming, model inference, and alert logic This technology greatly increases safety in sensitive areas, reduces reliance on humans, and improves surveillance dependability by automating danger identification through the use of computer vision and deep learning.
YOLOv5, Real-time Object Detection, Surveillance System, Deep Learning, Computer Vision, Threat Detection, Public Safety, Artificial Intelligence, Live Camera Feed, Hazardous Object Detection.
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