Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

Maverick Poma Rosales; Ciro Rodriguez; Yuri Pomachagua; Carlos Navarro

doi:10.1109/CICN51697.2021.9574690

Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

Maverick Poma Rosales, Ciro Rodriguez, Yuri Pomachagua, Carlos Navarro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	61-65
Number of pages	5
ISBN (Electronic)	9781728176956
DOIs	https://doi.org/10.1109/CICN51697.2021.9574690
State	Published - 22 Sep 2021
Event	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Peru Duration: 22 Sep 2021 → 23 Sep 2021

Publication series

Name	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conference

Conference	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
Country/Territory	Peru
City	Lima
Period	22/09/21 → 23/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Computer Vision
Convolutional Neural Networks
Deep Learning
Firearm Detection
Surveillance Cameras
YOLOv3
YOLOv5s

Access to Document

10.1109/CICN51697.2021.9574690

Cite this

Rosales, M. P., Rodriguez, C., Pomachagua, Y., & Navarro, C. (2021). Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks. In Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021 (pp. 61-65). (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN51697.2021.9574690

Rosales, Maverick Poma ; Rodriguez, Ciro ; Pomachagua, Yuri et al. / Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 61-65 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

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title = "Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks",

abstract = "The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.",

keywords = "Computer Vision, Convolutional Neural Networks, Deep Learning, Firearm Detection, Surveillance Cameras, YOLOv3, YOLOv5s",

author = "Rosales, {Maverick Poma} and Ciro Rodriguez and Yuri Pomachagua and Carlos Navarro",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 ; Conference date: 22-09-2021 Through 23-09-2021",

year = "2021",

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series = "Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021",

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Rosales, MP, Rodriguez, C, Pomachagua, Y & Navarro, C 2021, Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks. in Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021, Institute of Electrical and Electronics Engineers Inc., pp. 61-65, 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021, Lima, Peru, 22/09/21. https://doi.org/10.1109/CICN51697.2021.9574690

Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks. / Rosales, Maverick Poma; Rodriguez, Ciro; Pomachagua, Yuri et al.
Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 61-65 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

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Y1 - 2021/9/22

N2 - The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.

AB - The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.

KW - Computer Vision

KW - Convolutional Neural Networks

KW - Deep Learning

KW - Firearm Detection

KW - Surveillance Cameras

KW - YOLOv3

KW - YOLOv5s

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BT - Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Rosales MP, Rodriguez C, Pomachagua Y, Navarro C. Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks. In Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 61-65. (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). doi: 10.1109/CICN51697.2021.9574690

Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

Abstract

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Keywords

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