Title
Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks
Date Issued
22 September 2021
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
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.
Start page
61
End page
65
Language
English
OCDE Knowledge area
Sistemas de automatización, Sistemas de control Ingeniería de sistemas y comunicaciones
Scopus EID
2-s2.0-85119257644
Resource of which it is part
Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
ISBN of the container
9781728176956
Conference
13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 Lima 22 September 2021 through 23 September 2021
Sources of information: Directorio de Producción Científica Scopus