Deep Learning-Based Real-Time Weapon Detection System
Amjed A. Al-Mousa, Omar Z. Alzaibaq, Yazan Abu-Hashyeh · International Journal of Computing and Digital Systems · 2023
In recent years, the rate of gun violence has risen at a rapid pace.Most current security systems rely on human personnel to monitor lobbies and halls constantly.With the advancement of machine learning and, specifically, deep learning techniques, future closed-circuit TV (CCTV) and security systems should be able to detect threats and act upon this detection when needed.This paper presents a security system architecture that uses deep learning and image-processing techniques for real-time weapon detection.The system relies on processing a video feed to detect people carrying different types of weapons by periodically capturing images from the video feed.These images are fed to a convolutional neural network (CNN).The CNN then decides if the image contains a threat or not.If it is a threat, it would alert the security guards on a mobile application and send them an image of the situation.The system was tested and achieved a testing accuracy of 92.5%.Also, it was able to complete the detection in as fast as 1.6 seconds.