To make it possible for computer vision to self-train and comprehend visual input, pattern recognition algorithms are mainly used. Advanced measurements are required every time for the early detection of armed threats because of decreasing accidents and terrorist attacks. Weapon detection systems are mostly used in public spaces such as stadiums, airports, key squares, and battlefields, whether they are in urban or rural settings for better security objectives. Based on cloud architecture, DL, and ML algorithms are used by contemporary closed-circuit television surveillance and control systems to detect weapons. Using the Raspberry Pi as an edge device and the Efficient model to construct the weapons detection system, edge computing can be used to address these problems. The text report, including the image processing results, is sent to the cloud platform so that the operator can review it further. Soldiers can outfit themselves with the recommended edge node, headphones, and augmented reality glasses for visual data output to receive alerts about armed threats. Furthermore, we can improve our method's performance by adding more training data and changing the network architecture. The primary object of this paper is to build a model for detecting weapons such as pistols and rifles. The data will be taken from the Kaggle dataset. Our results and recommendations will help new researchers and related organisations.