The concept of a mobile system for detection fire phenomena based on convolutional neural networks

The research problem taken up in the article is the development of an efficient, mobile and effective fire detection algorithm based on the architecture of artificial neural networks. Both the process of training and inference of CNNs is burdened with a high demand for computing power. In the case of desktop devices, equipped with powerful processors and graphics cards, this process is largely facilitated and does not cause great difficulties. Another situation, however, is the desire to create a detection algorithm that in its performance will not differ from the stationary version, nevertheless its additional feature will be mobility. The desire to supervise vast areas of critical infrastructure using an unmanned aerial vehicle, imposes peculiar hardware limitations, which mainly include weight and size. The creation of an algorithm that will carry out real-time fire detection under the above-mentioned assumptions will therefore be a task that will require the optimization of a trained neural network model, into a format supported by popular mobile systems such as the Raspberry Pi.