Early Fire Detection and Segmentation Using Frame Differencing and Deep Learning Algorithms with an Indoor Dataset

Deep learning models, such as YOLOv5, well-known for object detection, and U-Net, used for segmentation, are known for their respective capabilities within computer vision tasks. In this study, the researchers introduced a novel framework that uses YOLOv5 and U-Net models, in combination with frame differencing techniques, to achieve early fire detection in an indoor setting. YOLOv5 was trained on a diverse dataset consisting of fire, smoke, and non-fire scenarios, while U-Net was exclusively trained on fire data. Motion detection was then implemented using frame differencing that allowed to identify fire movements effectively. The developed framework achieved an overall accuracy of 88%, outperforming the standalone YOLOv5 model with its 81% accuracy. This improvement of 7% in detection performance was influenced by the incorporation of fire motion analysis which effectively reduced false positive results. In summary, the study presents a robust framework that significantly improves fire detection in indoor environments with the help of motion analysis alongside the used deep learning models.