An intelligent and efficient approach for a weapon detection system using computer vision and edge computing

Pattern recognition algorithms have been used to make it possible for computer vision to self-train and comprehend visual input. Advanced measurements have been required every time for the early detection of armed threats in order to decrease accidents and terrorist attacks. Weapon detection systems have mostly been used in public spaces such as stadiums, airports, key squares, and battlefields, whether they are in urban or rural settings, to achieve better security objectives. Based on cloud architecture, DL and ML algorithms have been used by contemporary closed-circuit television surveillance and control systems to detect weapons. In addition to using the Raspberry Pi as an edge device and an efficient model to construct a weapons detection system, edge computing is used to address these problems. The text report includes the image processing results. Soldiers can outfit themselves with the recommended edge node and headphones, and the visual data output will allow them 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 model developed in this research detects weapons such as pistols and rifles in an average time of 1.30 s.