Graph Cuts and Deep Neural Networks for Fire Detection - Pereira, Davi Magalhães; Vieira, Marcelo Bernardes; Villela, Saulo Moraes (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2023)
Computer vision methods for fire detection have made significant advancements compared to traditional fire detection systems. The incorporation of fire segmentation masks enables precise analysis, offering valuable insights into the origin and spread of fires to prevent future incidents. This paper presents a novel approach that combines deep neural networks, graph cuts, and color thresholding to achieve fine-grained fire segmentation results. By incorporating graph cuts segmentation with global and local color information, our method enhances accuracy and detailed fire detection. As our results show, our method neatly improves recall, with a competitive precision, leading to an effective fire detection framework.