Weapon Detection Using Faster R-CNN

Abstract

Abstract: As criminal activities continue to rise, ensuring security has become a top priority across various sectors. Computer vision technology is being extensively employed to address this problem by detecting and monitoring abnormalities. Video surveillance systems capable of identifying and analyzing scenes and detecting anomalous events have become increasingly essential to safeguard personal belongings, ensure safety, and enhance security. Such systems play a crucial role in intelligence monitoring. In this study, we implemented automatic weapon (or gun) detection using Faster RCNN techniques. Two datasets were utilized: one consisting of pre-labeled photos, and the other a collection of manually labeled images. Upon analyzing the data, both algorithms yielded highly precise outcomes. However, the practicality of these systems in real-world scenarios will ultimately depend on the trade-off between time and accuracy