Deep Learning-Based Real-Time Weapon Detection with YOLOv8 for Intelligent Surveillance

The escalation of security threats has highlighted the implementation of intelligent surveillance systems with the ability to identify threats in advance. This study suggests a real-time deep learning-based weapon detection system with the YOLOv8, an object detection deep model. The system inspects real-time video streams for identifying and locating weapons such as guns and knives at high accuracy and low latency. By leveraging the improved-optimized architecture of YOLOv8, the model achieves high accuracy of detection with computational performance maintained high enough for real-time applications. Deep training on weapon dataset annotations enables strong performance in a broad surveillance setting. The developed methodology enhances situational awareness and provides secure autonomous response, and thus it is a great candidate for use in safety- critical environments like public transportation centers, schools, and critical infrastructure facilities.