Open Access

ARTICLE

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Erssa Arif^1,*, Syed Khuram Shahzad², Muhammad Waseem Iqbal³, Muhammad Arfan Jaffar⁴, Abdullah S. Alshahrani⁵, Ahmed Alghamdi⁶

1 Department of Computer Science, Superior University, Lahore, 54000, Pakistan
2 Department of Informatics & Systems, University of Management & Technology, Lahore, 54000, Pakistan
3 Department of Software Engineering, Superior University, Lahore, 54000, Pakistan
4 Faculty of Computer Science & Information Technology, Superior University, Lahore, 54000, Pakistan
5 Department of Computer Science & Artificial Intelligence, College of Computer Science & Engineering, University of Jeddah, Jeddah, 21493, Saudi Arabia
6 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21493, Saudi Arabia

* Corresponding Author: Erssa Arif. Email:

Computers, Materials & Continua 2022, 72(3), 4615-4630. https://doi.org/10.32604/cmc.2022.027571

Received 20 January 2022; Accepted 08 March 2022; Issue published 21 April 2022

Abstract

The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism. This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension. The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution. Efficient-Net algorithm gives better results than existing algorithms. By using Efficient-Net algorithms the accuracy achieved 98.12% when epochs increase as compared to other algorithms.

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Keywords

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