An Intelligent Deep Learning Framework for Real-Time Weapon Detection in Smart Surveillance Systems

Abstract

Weapon-related threats in public places demand intelligent surveillance systems capable of detecting dangerous objects accurately and in real time. This paper presents DeepGuard, an intelligent deep learning framework for automated weapon detection in images and surveillance videos using Faster Region-Based Convolutional Neural Network (Faster R-CNN) and Single Shot Detector (SSD). The proposed framework employs annotated weapon datasets for model training and utilizes convolutional neural networks to identify and localize weapons with bounding boxes. A comparative evaluation of SSD and Faster RCNN is conducted to analyze their detection accuracy and inference speed. Experimental results demonstrate that Faster R-CNN achieves superior detection accuracy, whereas SSD provides faster processing suitable for real-time applications. The developed system effectively identifies weapons in diverse surveillance environments, enhancing public safety through early threat detection and continuous monitoring. The proposed framework offers a reliable, scalable, and intelligent solution for smart surveillance systems, making it suitable for deployment in airports, railway stations, educational institutions, commercial buildings, and other high-security environments.