Electrical and Computer Engineering, Abdullah Gül University, Kayseri, Turkey.
Computer Engineering, Abdullah Gül University, Kayseri, Turkey.
PLoS One. 2022 Jun 3;17(6):e0269161. doi: 10.1371/journal.pone.0269161. eCollection 2022.
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.
如果仅使用空间特征，那么区分夜间视频中的火灾与非火灾物体是有问题的。由于摄像机的动态范围限制等多种因素，在低光照环境下，这些特征会受到严重干扰。这使得对夜间火灾的时间行为进行分析对于分类是必不可少的。为此，提出了一种基于 BLSTM 的夜间野火事件检测视频算法。实验表明，该算法在测试各种实际夜间野火事件记录时达到了 95.15%的准确率，并且每帧检测时间为 23.7 毫秒。此外，为了为这个具有挑战性的问题提供更有针对性的解决方案，本文还基于实验对可能导致错误预测的原因进行了深入调查，并讨论了夜间野火视频的独特性质。
