A Deep learning-Based Efficient Firearms Monitoring

Abstract

The increasing incidence of firearm-related violence in public spaces has created an urgent need for intelligent surveillance systems capable of real-time detection and monitoring. This study proposes a deep learning-based approach for reliable firearm detection using advanced convolutional neural networks (CNNs) integrated with real-time video analytics. The system is designed to automatically identify firearms such as pistols, rifles, and other weapons from surveillance camera feeds with high accuracy and minimal latency. By leveraging large-scale annotated datasets and transfer learning techniques, the model improves detection performance even in complex environments with occlusions, low lighting, and crowd density. Once a firearm is detected, the system triggers instant alerts to security authorities, enabling rapid response and preventive action. The proposed approach enhances public safety by reducing dependency on manual monitoring and improving the speed and reliability of threat detection systems. Experimental results demonstrate that the model achieves high precision and recall, making it suitable for deployment in smart city surveillance and critical infrastructure protection.