Weapon Detection Using YOLO V3 for Smart Surveillance System

• Published in: Mathematical problems in engineering Vol. 2021; pp. 1 - 9
• Main Authors: Narejo, Sanam, Pandey, Bishwajeet, Esenarro vargas, Doris, Rodriguez, Ciro, Anjum, M. Rizwan
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial neural networks; Automation; Datasets; Deep learning; Dictionaries; Firearms; Machine learning; Manslaughter; Mass murders; Mathematical problems; Neural networks; Object recognition; Rifles; Robots; Security systems; Surveillance; Training; Violence; Weapons
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2021/9975700

Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.