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Weapon Detection Using Deep Learning
This study presents an intelligent weapon detection system using the YOLO (You Only Look Once) deep learning model to automatically identify dangerous weapons such as guns, rifles, and knives in images and video streams. The system aims to enhance public safety by enabling real-time surveillance and early threat detection. A dataset containing various weapon images is used to train the model so that it can accurately classify and localize different types of weapons. The YOLO architecture performs object detection in a single stage, allowing faster processing compared to traditional multi-stage detection methods. During training, the model learns distinctive visual features of weapons to differentiate them from normal objects. The trained model is then integrated with a monitoring system capable of analyzing live camera feeds. When a weapon such as a gun, rifle, or knife is detected, the system generates an alert for security personnel. This approach helps reduce manual monitoring efforts and improves response time in critical situations. Experimental results show that the YOLO-based model achieves high detection accuracy with low latency. Therefore, the proposed system provides an effective solution for automated weapon detection in smart surveillance applications.
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