Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

中文翻译：

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使用YOLO V3进行武器监视的智能监控系统

每年，全世界有大量人口和解与枪支相关的暴力行为。在这项工作中，我们开发了一个基于计算机的全自动系统，以识别基本武器，特别是手枪和步枪。深度学习和迁移学习领域中的最新工作已证明在对象检测和识别领域取得了重大进展。通过在定制数据集上进行训练，我们已经实现了YOLO V3“您只看一次”的对象检测模型。训练结果证实YOLO V3优于YOLO V2和传统的卷积神经网络（CNN）。此外，由于我们使用转移学习来训练模型，因此在我们的方法中不需要密集的GPU或大量的计算资源。在我们的监控系统中应用此模型，我们可以尝试挽救人类生命，并减少误杀或大规模杀害的速度。此外，我们提出的系统还可以在高端监视和安全机器人中实施，以检测武器或不安全资产，从而避免对人类生命的任何形式的攻击或风险。