Design and Proposal of a Database for Firearms Detection

David Romero, Christian Salamea

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Closed circuit television (CCTV) surveillance systems that implement monitoring operators have multiple human limitations, these systems usually don’t provide an immediate response in different situations of danger like an armed robbery. To address this security gap, a firearms detection system has been developed through convolutional neural networks (CNNs). For its development a large database of images is necessary. This article presents the creation and characteristics of this database, which is made up of 247,576 images obtained from the web. This article addresses the application of different techniques for the creation of new images from the initial ones to increase the database, obtaining up to 22.7% relative improvement in the accuracy of the network after increasing the database. The database is structured into two classes. The first class is made up of people that have a gun and the second class of people not carrying a gun. The use of this database in the development of the detection system obtained up to 90% in “Precision” and “Recall” metrics in a convolutional neural network configuration based on “VGG net”, through the use of grayscale images.

Original languageEnglish
Title of host publicationAdvances in Emerging Trends and Technologies Volume 1
EditorsMiguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030320218
StatePublished - 1 Jan 2020
Event1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador
Duration: 29 May 201931 May 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Convolutional neural network
  • Database
  • Detection
  • Firearm


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