Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Deep Learning Based Model for Fire and Gun Detection

Authors
Ahmed Abdullah A. Shareef1, *, Pravin L. Yannawar1, Antar Shaddad H. Abdul-Qawy2, Hashem Al-Nabhi3, Ravindra B. Bankar4
1Vision and Intelligence System Lab, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, 431004, India
2Department of Mathematics and Computer Science, Faculty of Science, SUMAIT University, Zanzibar, Tanzania
3Department of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
4Department of Management Science, Dr.Babasaheb Ambedkar, Marathwada University, Aurangabad, Maharashtra, 431004, India
*Corresponding author. Email: shareef.kin@gmail.com
Corresponding Author
Ahmed Abdullah A. Shareef
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_32How to use a DOI?
Keywords
deep learning; computer vision; fire detection; pistol detection; gun detection; YOLOv5
Abstract

Real-time object detection is one of the most important applications for surveillance and a prominent computer vision task. This paper proposes a new deep learning-based model for fire, pistol, and gun detection in areas monitored by cameras like home fires, industrial explosions, and wildfires, as they happen frequently and cause adverse effects on the environment. Gun violence and mass shootings are also on the rise in certain parts of the world. Such incidents are time-sensitive and can cause a huge loss to life and property. Hence, the proposed work has built a deep learning model based on the YOLOv5 algorithm that processes a video frame-by-frame to detect such anomalies in real-time and generate an alert for the concerned authorities. Our model has validation with more speed and more accurate manner. The experimental result satisfies the goal of the proposed model and also shows a fast detection rate.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
978-94-6463-196-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_32How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ahmed Abdullah A. Shareef
AU  - Pravin L. Yannawar
AU  - Antar Shaddad H. Abdul-Qawy
AU  - Hashem Al-Nabhi
AU  - Ravindra B. Bankar
PY  - 2023
DA  - 2023/08/10
TI  - Deep Learning Based Model for Fire and Gun Detection
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 422
EP  - 430
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_32
DO  - 10.2991/978-94-6463-196-8_32
ID  - Shareef2023
ER  -