Comparative Analysis of Fire Detection Performance Using Deep Learning-Based Flame Segmentation Models

The development of computational models designed to minimize fire damage by detecting fires early from image data using deep-learning models has attracted considerable research attention. However, these models have not yet been widely refined for practical applications. Therefore, in this study, the performance of flame segmentation models based on deep learning was comparatively evaluated in terms of detection accuracy, inference speed, and size; these models were the U-Net, DeepLabV3+, and YOLOv8-seg. When 1,000 images with and without fire were quantitatively analyzed, DeepLabV3+ exhibited the best segmentation accuracy, with a mean intersection over union (mIoU) of 72.9% and an F1-score of 83.6%, whereas the U-Net and YOLOv8-seg achieved mIoUs of 70.5% and 63.1% and F1-scores of 81.9% and 76.4%, respectively. Furthermore, YOLOv8-seg demonstrated the best performance in real-time fire detection owing to its lightweight architecture, achieving an inference speed of 145.2 fps with approximately 3.26 million parameters. These findings indicate that models for designing image-based real-time fire detection systems should be selected by comprehensively examining their accuracy, prediction speed, and size while considering the hardware performance of the intended detection device.