WEAPON DETECTION USING ARTIFICIAL INTELLIGENCE DEEP LEARNING FOR SECURITY APPLICATIONS

Abstract

In recent years, ensuring public safety and security has become a major concern due to the increasing incidents involving weapons in public and private spaces. This paper presents an intelligent weapon detection system using Artificial Intelligence (AI) and deep learning techniques for real-time security applications. The proposed system employs advanced object detection algorithms such as Convolutional Neural Networks (CNN) and YOLO (You Only Look Once) to accurately identify dangerous objects like guns and knives from live video streams captured through surveillance cameras. The system processes video frames in real-time and highlights detected weapons using bounding boxes, enabling security personnel to quickly recognize potential threats. An alert mechanism, including buzzer activation or notification alerts, is triggered immediately upon detection, ensuring rapid response and preventive action. This reduces reliance on manual monitoring and minimizes human error, thereby improving overall surveillance efficiency. Furthermore, the integration of cost-effective hardware such as ESP32-CAM enhances the system’s affordability and scalability, making it suitable for deployment in diverse environments such as schools, public areas, offices, and transportation hubs. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in weapon detection while maintaining real-time performance. Overall, the AI-based weapon detection system provides a smart, efficient, and scalable solution to modern security challenges, significantly improving threat detection and response time.