Research Specifications

Fire is a recurring event that usually causes a lot of social, environmental, ecological, and economic damage in different environments. Therefore, machine vision-based fire detection can be one of the most important tasks in modern surveillance systems. Most of the existing computer vision-based fire detection methods are only able to detect a single case of flame or smoke. In this research, a unified flame and smoke detection approach, termed “UFS-Net,” based on deep learning is proposed. An efficient and tailored convolutional neural network architecture is designed to detect both fire flames and smoke in video frames. UFS-Net is capable of identifying fire hazards by classifying video frames into eight classes: 1) flame, 2) white smoke, 3) black smoke, 4) flame and white smoke, 5) flame and black smoke, 6) black smoke and white smoke, 7) flame, white smoke and black smoke, and 8) normal status. To further increase the reliability of UFS-Net, a decision module based on a voting scheme is applied. In addition, a rich annotated dataset named “UFS-Data” that includes 849,640 images and 26 videos, captured/collected from various data sources and artificial images made in this research, is prepared for training and evaluation of UFS-Net. Extensive experiments conducted on “UFS-Data” and other benchmark datasets (i.e., “Mivia,” “BoWFire,” and “FireNet”), and the comparisons with state-of-the-art methods, confirm the high performance of UFS-Net. All the implementation source codes and the “UFS-Data” are made publicly available at https://github.com/alihosseinice/UFS-Net.