在这项工作中，我们探索了不同的卷积神经网络（CNN）架构及其在视频或静止图像中非临时性二进制火灾检测和定位的变体。我们考虑了通过实验定义的，降低复杂性的深度CNN架构的性能，并评估了应用于不同CNN架构的不同优化和规范化技术的效果（跨越了Inception，ResNet和EfficientNet架构概念）。..

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts).Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection.