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，导致其有效性受限。本研究采用卷积神经网络与迁移学习技术，开发了一套用于自动手枪检测的实时计算机视觉系统。通过对现有在线手枪检测方法的综合分析，重点解决了误报率与学习时长问题。研究表明迁移学习是一种有效途径。尽管存在技术挑战，所提出系统仍实现了84.74%的精确率，展现出与相关研究相媲美的优异性能，可支持更快速的学习与精准的自动手枪检测以增强安全性。本研究通过减少对人眼监控的依赖推进了安保措施发展，彰显了基于迁移学习的方法在实现高效可靠手枪检测方面的潜力。