Vision-based smoke detector

  • Authors

    • Abdullah Ali Mohammed Noman International Islamic University Malaysia
    • Zaw Zaw Htike
    https://doi.org/10.14419/ijet.v7i4.17955

    Received date: August 19, 2018

    Accepted date: May 24, 2019

    Published date: July 22, 2019

  • Convolutional Neural Network, Rectified Linear Unit, You Only Look Once, Artificial Neural Network.

  • Abstract

    Previous studies have documented the significant applications of the electronic smoke detector. With the capabilities of vision based fire detection and increase in the number of surveillance cameras, a lesser attention is given to the vision-based type smoke detector. Moreover, some drawbacks have been identified in the accuracy and efficiency of smoke detection. The present study proposes a vision based smoke detector to overcome the shortcomings of the current traditional electronic and vision based smoke detectors. A Convolutional Neural Network is used to classify the smoke regions. After testing the proposed method, the accuracy was approximately 94%. When a modern approach of object detection is used to support image classifying, its accuracy increases by 96%.

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  • How to Cite

    Ali Mohammed Noman, A., & Zaw Htike, Z. (2019). Vision-based smoke detector. International Journal of Engineering and Technology, 7(4), 6934-6936. https://doi.org/10.14419/ijet.v7i4.17955