Edge Intelligence-Assisted Smoke Detection in Foggy Surveillance Environments

Khan Muhammad, Salman Khan, Vasile Palade, Irfan Mehmood, Victor Hugo C. De Albuquerque

Research output: Contribution to journalArticle

6 Citations (Scopus)
61 Downloads (Pure)

Abstract

Smoke detection in foggy surveillance environments is a challenging task and plays a key role in disaster management for industrial systems. The current smoke detection methods are applicable to only normal surveillance videos, providing unsatisfactory results for video streams captured from foggy environments, due to challenges related to clutter and unclear contents. In this paper, an energy-friendly edge intelligence-assisted smoke detection method is proposed using deep convolutional neural networks for foggy surveillance environments. Our method uses a light-weight architecture, considering all necessary requirements regarding accuracy, running time, and deployment feasibility for smoke detection in an industrial setting, compared to other complex and computationally expensive architectures including AlexNet, GoogleNet, and visual geometry group (VGG). Experiments are conducted on available benchmark smoke detection datasets, and the obtained results show better performance of the proposed method over state-of-the-art for early smoke detection in foggy surveillance.

Original languageEnglish
Article number8709763
Pages (from-to)1067 - 1075
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number2
Early online date8 May 2019
DOIs
Publication statusPublished - Feb 2020

Bibliographical note

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Keywords

  • Artificial intelligence
  • convolutional neural networks (CNN)
  • edge intelligence
  • foggy surveillance environment
  • smoke detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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