Abstract
This work evaluates modern convolutional neural networks (CNN) for the task of smoke detection on image data. The networks that were tested are AlexNet, Inception-V3, Inception-V4, ResNet, VGG, and Xception. They all have shown high performance on huge ImageNet dataset, but the possibility of using such CNNs needed to be checked for a very specific task of smoke detection with a high diversity of possible scenarios and a small available dataset. Experimental results have shown that inception-based networks reach high performance when samples in the training dataset cover enough scenarios while accuracy dramatically drops when older networks are utilized.
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