Transfer Learning-based Real-time Handgun Detection
This research addresses the problem of reducing human monitoring dependence in surveillance systems for security applications, but it is incremental as it builds on existing transfer learning methods.
The study tackled real-time automatic handgun detection using convolutional neural networks and transfer learning, achieving a precision rate of 84.74% and enabling faster learning for enhanced security.
Traditional surveillance systems rely on human attention, limiting their effectiveness. This study employs convolutional neural networks and transfer learning to develop a real-time computer vision system for automatic handgun detection. Comprehensive analysis of online handgun detection methods is conducted, emphasizing reducing false positives and learning time. Transfer learning is demonstrated as an effective approach. Despite technical challenges, the proposed system achieves a precision rate of 84.74%, demonstrating promising performance comparable to related works, enabling faster learning and accurate automatic handgun detection for enhanced security. This research advances security measures by reducing human monitoring dependence, showcasing the potential of transfer learning-based approaches for efficient and reliable handgun detection.