Smart Firearm Recognition Framework for Live Monitoring Using Advanced Neural Network

The project “Smart Firearm Recognition Framework for Live Monitoring Using Advanced Neural Networks” aims to develop a robust and efficient system for identifying weapons, specifically handguns and knives, through the application of advanced Advanced Neural Networks techniques. Implemented using Python as the primary coding language, the project leverages the Flask web framework to deliver an interactive and user-friendly interface, complemented by HTML, CSS, and JavaScript for front-end development. The core of the Recognition mechanism is built upon the YOLOv8 (You Only Look Once version 8) architecture, a state-of-the-art object Recognition model known for its high speed and accuracy. Despite the complexity of the task, the model achieves an overall accuracy of 64%, a notable performance given the challenging nature of weapon Recognition in varied environments. The training dataset comprises approximately 4000 images, focusing exclusively on handguns and knives, ensuring that the model is wellcalibrated to recognize these specific threats. This dataset is meticulously curated to include a diverse array of scenarios and perspectives, enhancing the model’s ability to generalize across different contexts. The system supports three distinct Recognition modes: static image Recognition, video stream analysis, and real-time Recognition via webcam. This multi-faceted approach ensures flexibility and applicability in various use cases, from security screening and Monitoring to automated threat Recognition systems. Overall, this project represents a significant step forward in the application of Advanced Neural Networks for public safety and security, providing a scalable and efficient solution for weapon Recognition across multiple platforms and scenarios.