Abstract

Gas leaks can cause sever air contamination and are potentially harm to human health and the environment. The use of mobile robot technology in areas where gas leakage often occur, can reduce the risk of harm to human workers. Mobile robots can be equipped with accurate and precise gas sensors to provide information on the position of gas sources. However, the problem in detecting the basis of a gas leak is the presence of outside airflow, which causes dispersion this, can make gas detection difficult because it causes the gas concentration to decrease at the position of the leak source. In this research, a mobile robot was developed to determine the location of the origin of gas leaks using a thermal imaging camera and the YoloV5 algorithm. YoloV5 is a deep learning algorithm based on Convolutional Neural Network (CNN) to perform object detection in one step. The results of experiments show that the mobile robot is able to accurately find the location of the gas leak with a success rate of 91.25%.

Original languageEnglish
Title of host publicationProceeding - IEEE 9th Information Technology International Seminar, ITIS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350306835
DOIs
Publication statusPublished - 2023
Event9th IEEE Information Technology International Seminar, ITIS 2023 - Batu Malang, Indonesia
Duration: 18 Oct 202320 Oct 2023

Publication series

NameProceeding - IEEE 9th Information Technology International Seminar, ITIS 2023

Conference

Conference9th IEEE Information Technology International Seminar, ITIS 2023
Country/TerritoryIndonesia
CityBatu Malang
Period18/10/2320/10/23

Keywords

  • CNN
  • Mobile robot
  • YOLOV5
  • air contamination
  • thermal camera

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