Comparative study of modern convolutional neural networks for smoke detection on image data

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.