Weapon Detection Using YOLO V3 for Smart Surveillance System

Sanam Narejo, Bishwajeet Pandey, Doris Esenarro Vargas, Ciro Rodriguez, M. Rizwan Anjum

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish
Article number9975700
JournalMathematical Problems in Engineering
StatePublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Sanam Narejo et al.


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