An Improved Approach for Fire Detection using Deep Learning Models

Abstract

Fire detection using computer vision techniques and image processing has been a topic of interest among the researchers. Indeed, good accuracy of computer vision techniques can outperform traditional models of fire detection. However, with the current advancement of the technologies, such models of computer vision techniques are being replaced by deep learning models such as Convolutional Neural Networks (CNN). However, many of the existing research has only been assessed on balanced datasets, which can lead to the unsatisfied results and mislead real-world performance as fire is a rare and abnormal real-life event. Also, the result of traditional CNN shows that its performance is very low, when evaluated on imbalanced datasets. Therefore, this paper proposes use of transfer learning that is based on deep CNN approach to detect fire. It uses pre-trained deep CNN architecture namely VGG, and MobileNet for development of fire detection system. These deep CNN models are tested on imbalanced datasets to imitate real world scenarios. The results of deep CNNs models show that these models increase accuracy significantly and it is observed that deep CNNs models are completely outperforming traditional Convolutional Neural Networks model. The accuracy of MobileNet is roughly the same as VGGNet, however, MobileNet is smaller in size and faster than VGG.