Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier

Marks Dextre, Oscar Rosas, Jesus Lazo, Juan C. Gutiérrez

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

Abstract

Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia’s Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.

Original languageEnglish
Title of host publicationProceedings - 2021 47th Latin American Computing Conference, CLEI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665495035
DOIs
StatePublished - 2021
Event47th Latin American Computing Conference, CLEI 2021 - Virtual, Cartago, Costa Rica
Duration: 25 Oct 202129 Oct 2021

Publication series

NameProceedings - 2021 47th Latin American Computing Conference, CLEI 2021

Conference

Conference47th Latin American Computing Conference, CLEI 2021
Country/TerritoryCosta Rica
CityVirtual, Cartago
Period25/10/2129/10/21

Bibliographical note

Publisher Copyright:
©2021 IEEE

Keywords

  • Edge computing
  • Gun detection
  • Jetson AGX Xavier
  • Object detection
  • Real-Time
  • YOLOv5

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