Two-stage deep learning-based video image recognition of early fires in heritage buildings

Abstract

Fire poses a huge safety risk to heritage or historic buildings. Traditional approaches are unsuitable because of their high false alarm rates, delayed features, and vulnerability in heritage building environments. To reduce the serious consequences caused by fires, it is necessary to develop advanced approaches to achieve real-time early-fire detection in heritage buildings. Hence, this study proposed a two-stage deep learning-based video image recognition of real-time fires in heritage buildings. Motion detection based on a Gaussian mixture model was applied to determine moving objects in the first stage. Then, a Fire-Det model for the indoor environment and a Fire-Det Nano model for outdoor areas were developed in the second stage. To validate the proposed models, a dataset including negative images was built and tested. To illustrate the performance of this study, the proposed approaches were compared with traditional methods. It is found that the Fire-Det model can complete fire detection at the fastest reasoning speed without losing generalizability of accuracy. The proposed Fire-Det Nano model can also drastically reduce computation time at the expense of a slight reduction in accuracy. Fire-Det is the innovation in AI. Fire-Det Nano and motion detection are applications in engineering. These findings indicate that the proposed method has great potential significance in the application of early-fire detection in heritage buildings, such as the Forbidden City.