Flame Image Classification Based on Deep Learning and Three-Way Decision-Making

The classification and recognition of flame images play an important role in avoiding forest fires. Deep learning technology has shown good performance in flame image recognition tasks. In order to further improve the accuracy of classification, this paper combines deep learning technology with the idea of three-way decision-making. First, a ResNet34 network is used for initial classification. The probability value calculated by the SoftMax function is used as the decision evaluation criterion for initial classification. Using the idea of three-way decision-making, the flame image is divided into positive domain, negative domain, and boundary domain based on decision evaluation indicators. Furthermore, we perform secondary classification on images divided into boundary domains. In the secondary classification, a DualArchClassNet structure was constructed to extract new features and combine them with the features of the initial classification. The integrated features are optimized and used to reclassify images in uncertain domains to improve overall classification accuracy. The experimental results show that the proposed method improves the accuracy of flame image recognition compared to using a single ResNet34 network.