WEAPON DETECTION MODEL USING DEEP LEARNING
Abstract
The increasing threat of violence in public spaces necessitates the development of effective surveillance systems. This study aims to design a deep learning-based weapon detection model for accurate identification of firearms and other weapons in real-time video footage. Leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this research evaluates the performance of various models and explores techniques for data augmentation, transfer learning, and addressing ethical concerns. The proposed system has significant implications for enhancing public safety and security.
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