Firearm Detection using Convolutional Neural Networks

This papers studies the application of the YOLO algorithm to create a firearm detection system, demonstrating its effectiveness in this task. We also constructed a dataset based on the website Internet Movie Firearm Database (IMFDB) for this study. Individuals carrying firearms in public places are a strong indicator of dangerous situations. Studies show that a rapid response from law enforcement agents is the main factor in reducing the number of victims. However, a large number of cameras to be monitored leads to an overload of CCTV operators, generating fatigue and stress, consequently, loss of efficiency in surveillance. Convolutional neural networks have been shown to be efficient in the detection and identification of objects in images, having sometimes produced more accurate and consistent results than human candidates.