Agirman, Ahmet KTasdemir, Kasim2022-07-212022-07-21202219326203https://doi.org/10.1371/journal.pone.0269161https://hdl.handle.net/20.500.12573/1326Distinguishing 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.enginfo:eu-repo/semantics/openAccessAlgorithmsCommunications MediaFiresWildfiresarticle176126