(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Gun-related violence poses a major threat to public safety and well-being worldwide. Traditional surveillance systems rely on human monitoring, which is prone to fatigue and delayed response. This paper proposes a smart surveillance system for real-time weapon detection using deep learning techniques. The system employs the YOLOv3 algorithm to detect guns, rifles, and fire from video streams with high accuracy. It also captures the location of incidents and stores data for further analysis. A multi-system architecture is implemented using socket programming to simulate real-world integration. The proposed model ensures fast detection with reduced computational cost. It enhances response time and minimizes human dependency in surveillance tasks. The system can be extended to autonomous security and robotic applications. Experimental results demonstrate its effectiveness in improving public safety and security systems.