有效的室内火灾探测与频道洗牌模块

Hao Ge, Yichao Cao, Xiaobo Lu
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引用次数: 0

摘要

近年来,基于计算机视觉和深度学习的方法成为火灾探测的主流方法。然而,3D卷积神经网络(CNN)昂贵的计算成本难以承受,难以及时捕获视频的五个区域。在本文中,我们设计了一个基于二维CNN的信道洗牌模块(CSM),以保持计算成本和精度之间的平衡。通过融合RGB帧和差分帧,CSM提高了二维CNN提取时间信息的能力,大大降低了基于三维CNN的方法的成本。提出了四种不同的CSM结构,并根据实验结果选择了最佳的CSM结构。实验还证明,CSM在序列分类方面提高了TSN和TSM的性能。基于CSM的TSM模型准确率为99.2045%,假阳性率为0.7890%,假阴性率为0.4530%,证明了CSM模型在时间特征建模中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective Indoor Fire Detection with Channel Shuffle Module
In recent years, methods based on computer vision and deep learning become the mainstream approaches in fire detection. However, the expensive computation cost of 3D convolutional neutral network (CNN) is unbearable and it is difficult for them to capture the fire regions of videos in time. In this paper, we design a module named channel shuffle module (CSM) based on 2D CNN to keep the balance between computation cost and accuracy. By fusing RGB frame and differential frame, CSM improves the ability of 2D CNN in temporal information extraction which much less cost than methods based on 3D CNN. Four different structures of CSM are proposed and we choose the best one by experiment results. Also, experiments prove that the performances of TSN and TSM are improved with CSM in sequence classification. The accuracy of TSM with CSM is 99.2045%, false positive rate reaches 0.7890% and false negative rate reaches 0.4530%, which demonstrates the efficiency of CSM in temporal feature modeling.
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