A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities

The rise in firearm-related incidents in urban areas has intensified the need for intelligent surveillance systems capable of preventing threats before they escalate. With the growth of smart city infrastructure, leveraging artificial intelligence, particularly deep learning, has emerged as a viable solution for real-time monitoring and threat detection. This paper proposes a deep learning-based technique for efficient firearm detection within public surveillance systems to ensure enhanced security in smart cities. Using advanced convolutional neural networks (CNNs), the proposed method detects firearms in live video feeds with high accuracy and low latency. The system is designed to work efficiently in diverse environments, addressing challenges such as variable lighting, occlusion, and varying angles. Extensive experiments demonstrate the system’s effectiveness in recognizing firearms across multiple scenarios, indicating its potential as a real-time security enhancement tool for smart city deployments.