Fire detection in video surveillances using convolutional neural networks and wavelet transform

Fire is one of the most frequent and common emergencies threatening public safety and social development. Recently, intelligent fire detection technologies represented by convolutional neural networks (CNNs) have been widely concerned by academia and industry, substantially improving detection accuracy. However, CNN-based fire detection systems are still subject to the interference of false alarms and the limitation of computing power. In this paper, taking advantage of traditional spectral analysis in fire image detection technology, a novel Wavelet-CNN method is proposed, which applies the 2D Haar transform to extract spectral features of the image and input them into CNNs at different layer stages. Two classic backbone networks, ResNet50 and MobileNet v2 (MV2) are used to test our method, and experimental results on a benchmark fire dataset and a video dataset show that the method improves fire detection accuracy and reduces false alarms, especially for the light-weight MV2. Despite the low computational needs, the Wavelet-MV2 achieves accuracy that is comparable to state-of-the-art methods.