The detection of handguns from live-video in real-time based on deep learning - Mohammed Ghazal; Najwan Waisi; Nawal Abdullah (TELKOMNIKA (Telecommunication Computing Electronics and Control), 2020)
Many people have been killed indiscriminately by the use of handguns in different countries. Terroristacts, online fighting games and mentally disturbed people are considered the common reasons for these crimes.  A real-time handguns detection surveillance system is built to overcome these badacts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue,the designed detection system sends an alert message to the supervisor when aweapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgundetection operation. This model has been selected becauseit is fast and accurate in infering to integrate network for detecting and classifying weaponsin images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model bothenhance the accuracy level in identifying the real time handguns detection.