Every year, a large amount of the 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 knives and guns. 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 the YOLOv5 object detection model by training it on our customized dataset using google colab. 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 a 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.

Every year, a large amount of the 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 knives and guns. 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 the YOLOv5 object detection model by training it on our customized dataset using google colab. 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 a 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.