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.

翻译：传统监控系统依赖人工注意力，限制了其有效性。本研究利用卷积神经网络与迁移学习，开发了一种用于自动手枪检测的实时计算机视觉系统。对在线手枪检测方法进行了全面分析，重点强调降低误检率与学习时间。研究表明迁移学习是一种有效方法。尽管存在技术挑战，所提出的系统仍实现了84.74%的精确率，展现出与相关研究相当的性能，能够实现更快速的学习与准确的自动手枪检测，从而增强安全性。本研究通过减少对人工监控的依赖推动了安全措施的发展，展示了基于迁移学习的方法在实现高效可靠的手枪检测方面的潜力。