Authors : Ajay S. Chhajed; Hetakshi R. Borse; Vaishnavi S. Bongane; Shrushti P. Bodake; Aachal N. Borle; Sarthak S. Bote; Prathmesh S. Birajdar

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/2p9hu356

Scribd : https://tinyurl.com/53mcsueh

DOI : https://doi.org/10.38124/ijisrt/25oct471

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Abstract : In recent years, the rise of security threats in public and private spaces has emphasized the need for intelligent surveillance systems. This research presents a real-time AI-based threat detection model that identifies potential hazards such as guns, knives, and masks using a customized YOLOv8 architecture integrated with OpenCV. The system is designed to differentiate threatening and non-threatening objects across 27 classes, providing immediate alerts through a web-based dashboard and voice notifications. The application, built using Flask, JavaScript, and SQLite, offers a live camera feed and automated logging of detected threats with time and date. Achieving an accuracy of 90% and high frame-rate inference, the system demonstrates strong potential for real-world deployment in smart surveillance, ensuring rapid and automated responses to life-threatening events.

Keywords : Object Detection, Computer Vision, Real Time Surveillance, Threat Identification, Deep Learning.

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