Video Fire Detection Using Semantic Segmentation Inference Results

We investigate a method for video fire detection that contributes to rapid fire extinguishing, using semantic segmentation based on deep learning with images. In this study, we aim to detect fires in video footage from existing surveillance cameras, focusing on the detection of smoke with a transparent background shortly after ignition. In this paper, we investigate fire detection using semantic segmentation, which does not require training on images of fires or smoke. By utilizing the inference results from PSPNet, which was trained on the ADE20K dataset, we demonstrate that the pixel-wise class and confidence vary depending on the presence of flames and smoke.