Firearm Detection using Convolutional Neural Networks

Rodrigo Kanehisa, Areolino Neto

Abstract

This papers studies the application of the YOLO algorithm to create a firearm detection system, demonstrating its effectiveness in this task. We also constructed a dataset based on the website Internet Movie Firearm Database (IMFDB) for this study. Individuals carrying firearms in public places are a strong indicator of dangerous situations. Studies show that a rapid response from law enforcement agents is the main factor in reducing the number of victims. However, a large number of cameras to be monitored leads to an overload of CCTV operators, generating fatigue and stress, consequently, loss of efficiency in surveillance. Convolutional neural networks have been shown to be efficient in the detection and identification of objects in images, having sometimes produced more accurate and consistent results than human candidates.

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Paper Citation


in Harvard Style

Kanehisa R. and Neto A. (2019). Firearm Detection using Convolutional Neural Networks.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 707-714. DOI: 10.5220/0007397707070714


in Bibtex Style

@conference{icaart19,
author={Rodrigo Kanehisa and Areolino Neto},
title={Firearm Detection using Convolutional Neural Networks},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={707-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007397707070714},
isbn={978-989-758-350-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Firearm Detection using Convolutional Neural Networks
SN - 978-989-758-350-6
AU - Kanehisa R.
AU - Neto A.
PY - 2019
SP - 707
EP - 714
DO - 10.5220/0007397707070714