论文标题
非颞实时火灾检测的高效且紧凑的卷积神经网络体系结构
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
论文作者
论文摘要
自动视觉火灾检测用于补充传统的火灾检测传感器系统（烟雾/热）。在这项工作中，我们研究了不同的卷积神经网络（CNN）架构及其变体，用于在视频（或静止）图像中检测火力像素区域的非颞实时界限。通过实验分析提出了两种降低的复杂性紧凑型CNN体系结构（Nasnet-a-o-a-o-a-in-fire和Shufflenetv2-onfire），以优化此任务的计算效率。结果改善了当前的最新检测解决方案，用于全帧二进制分类的精度为95％，超像素定位的精度为97％。对于二进制分类，我们显然将分类速度提高了2.3倍，超级像素定位的速度分别为1.3倍，分别为40 fps和18 fps的运行时，在现场表现出了高效，稳健且实时的解决方案，用于火灾区域检测。随后在低功率设备上实现（NVIDIA Xavier-NX，通过ShuffLenetv2-onfire实现49 fps用于全帧分类），证明我们的体系结构适用于各种现实世界中的部署应用程序。
Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bounds detection of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architectures (NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental analysis to optimise the computational efficiency for this task. The results improve upon the current state-of-the-art solution for fire detection, achieving an accuracy of 95% for full-frame binary classification and 97% for superpixel localisation. We notably achieve a classification speed up by a factor of 2.3x for binary classification and 1.3x for superpixel localisation, with runtime of 40 fps and 18 fps respectively, outperforming prior work in the field presenting an efficient, robust and real-time solution for fire region detection. Subsequent implementation on low-powered devices (Nvidia Xavier-NX, achieving 49 fps for full-frame classification via ShuffleNetV2-OnFire) demonstrates our architectures are suitable for various real-world deployment applications.
