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    BLSTM based night-time wildfire detection from video
    (Public Library of Science, 2022) Agirman, Ahmet K; Tasdemir, Kasim; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Ağırman, Ahmet K.; Taşdemir, Kasım
    Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.