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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Pages: 72-78

Smoke detection from foggy environment based on color spaces

Mehmet Erdal ÖZBEK fingerprint•
Uğur Emre YILDIZ fingerprint
Publication DateSeptember 30, 2021
Volume / IssueVol. 9, No. 3 (pp. 72-78)
subject

Abstract

Detection of smoke from videos captured by surveillance cameras in outdoor environments is one of the useful outcome of Internet of Things (IoT) applications. The potential benefit increases when deep learning (DL) architectures are involved. However, an inherent difficulty is to detect smoke while natural events like fog exists. The effectiveness of color spaces in detection performance has not yet fully evaluated in those architectures. Moreover, the energy and memory requirements of DL architectures may not be applicable for handling IoT implementation demands. Therefore, in this work, a DL architecture with a suitable color space model, applicable for IoT implementations is proposed to detect smoke from videos in foggy environment. By collecting several videos including smoke samples, the performance comparison of popular and the state-of-the-art DL architectures denoted the outperforming result according to both accuracy and memory usage.
Keywords:Color spaceDeep learningIoTSmoke detection

Author Affiliations

  • Mehmet Erdal ÖZBEK — İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜfingerprint0000-0001-5840-7960
  • Uğur Emre YILDIZ — İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜfingerprint0000-0003-1166-4542
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How to Cite This Article

M. E. Ö., U. E. Y. (2021). Smoke detection from foggy environment based on color spaces. International Journal of Applied Methods in Electronics and Computers, 72-78. https://doi.org/10.18100/ijamec.973440