<![CDATA[This research proposes an intelligent security system based on computer vision for real-time detection of weapons and military equipment in guard posts and military bases. The primary objective is to strengthen early warning capabilities by automatically identifying objects resembling firearms, knives, or combat gear through surveillance cameras. The system employs convolutional neural networks (CNN) for object classification and detection, integrated with a real-time alert mechanism to notify security personnel when suspicious items are detected. The method includes dataset collection of various weapon and combat equipment images, preprocessing, model training using YOLOv8, and evaluation with precision, recall, and F1-score metrics. Experimental results demonstrate that the system can accurately recognize specific military-related objects with high detection speed, ensuring reliable performance in real-time monitoring scenarios. The findings highlight the potential application of artificial intelligence in enhancing situational awareness and proactive security measures in military environments. This study concludes that the implementation of computer vision-based intelligent detection systems can significantly improve the effectiveness of base and post security operations, providing timely alerts to prevent potential threats]]>
Copyrights © 2025
