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 journalArticlepeer-review

    62 Citations (Scopus)
    352 Downloads (Pure)


    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
    Issue number2
    Early online date8 May 2019
    Publication statusPublished - Feb 2020

    Bibliographical note

    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Paper is not published yet, but in print, and available online as early access, on the journal web site while waiting to be printed.


    • 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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