Identification of Ammunition in Public Environment Using Deep Learning Techniques

Effective automated security systems are in high demand due to the rise in criminal activity. The deep learning model presented in this work is intended to provide real-time threat identification by identifying and categorizing different kinds of weapons from surveillance data. The model, which was trained on a bespoke dataset that included seven different weapon categories-such as assault rifles, grenades, and pistols-was based on the VGGNet architecture and achieved a 98.4 % accuracy rate. Preprocessing techniques including background removal and rotation modifications were used on the dataset photos to increase robustness. The model demonstrated improved classification accuracy and recall metrics when tested against well-known architectures such as VGG-16, ResNet-50, and ResNet-101. By giving security staff prompt notifications of possible dangers in monitored areas, this technology has useful applications in automated monitoring therefore strengthening preventative security measures, Additional optimization for real-time deployment and integration with intricate security systems will be investigated in future study.