Abstract This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bounding box pixels) algorithm, is devised to automatically annotate the bounding box that includes the smoke with fuzzy boundaries. The model parameters of the MTB algorithm are examined by multifactor analysis of variance. To demonstrate the validity of the proposed approach, a case study is provided that examines real video clips of incipient forest fires with small amounts of smoke. The performance of the proposed approach is compared with those of existing deep learning models, including convolutional neural network (CNN), faster region-based CNN (faster R-CNN), and SlowFast. It is demonstrated that the proposed approach achieves enhanced detection accuracy, while reducing false negative rates.

中文翻译：

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基于视频的 SlowFastMTB 模型，用于检测初期森林火灾中的少量烟雾

摘要 本文提出了一种基于视频的SlowFast模型，将SlowFast深度学习模型与新的边界框标注算法相结合。新算法，即MTB（即移动对象像素数与边界框像素数之比）算法，旨在自动注释包含模糊边界的烟雾的边界框。MTB算法的模型参数通过多因素方差分析来检验。为了证明所提出方法的有效性，提供了一个案例研究，该案例研究检查了带有少量烟雾的初期森林火灾的真实视频剪辑。将所提出方法的性能与现有深度学习模型的性能进行比较，包括卷积神经网络 (CNN)、更快的基于区域的 CNN (faster R-CNN) 和 SlowFast。