In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.
SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes
Beijing Jiaotong University（北京交通大学）；Dalian Polytechnic University（大连理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Dalian Martime University（大连海洋大学）；Nanjing University of Science and Technology（南京理工大学）
Fire and Smoke Detection with Burning Intensity Representation
School of Software Technology, Zhejiang University（浙江大学软件学院）；China Mobile (Suzhou) Software Technology Co., Ltd.（中国移动（苏州）软件技术有限公司）；University of Trento（特伦托大学）；Hefei University of Technology（合肥工业大学）
AURA: A Hybrid Spatiotemporal-Chromatic Framework for Robust, Real-Time Detection of Industrial Smoke Emissions
Department of Integrative Systems and Design, Hong Kong University of Science and Technology（整合系统与设计系，香港科技大学）；AI Innovation Hub, ANTEI Limited（人工智能创新中心，ANTEI有限公司）；Institute of Applied Computer Science, ITMO University（应用计算机科学研究院，ITMO大学）
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets
Spatial Informatics Group, LLC.（空间信息集团有限公司）；Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；University of California at Berkeley（加州大学伯克利分校）；Fowler School of Engineering, Chapman University（查普曼大学福勒工程学院）；Machine Learning and Assistive Technology Lab (MLAT), Chapman University（机器学习与辅助技术实验室（MLAT），查普曼大学）
Early Fire Detection Using HEP and Space-time Analysis
Southwest Jiaotong University（西南交通大学）
本文通过对现场烟气的监测，研制了一种基于视频的火灾早期报警系统。在这项工作中有两个主要贡献。首先，找到最好的纹理特征烟雾检测，一个通用的框架，命名为直方图的等效模式（HEP），实现了广泛的各种纹理特征的评价。其次，提出了描述烟雾时空特性的\emph{Block based Inter-Frame Difference}（BIFD）和改进的LBP-TOP模型。为了减少误报，利用烟雾历史图像（SHI）登记候选烟雾块的最近分类结果。实验结果表明，与现有的方法相比，该方法具有更高的准确率和更少的虚警率。
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology Bhopal（数学、生物信息学与计算机应用系， Maulana Azad 国家理工学院 Bhopal）；The Robotics and Mechatronics Group, University of Twente（机器人与机电组， Twente 大学）
Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
Brno University of Technology（布尔诺理工大学）；VSB - Technical University of Ostrava（俄斯特拉发技术大学）；University of Ostrava（俄斯特拉发大学）；Mendel University in Brno（布尔诺孟德尔大学）
A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification
Department of Electrical and Computer Engineering, University of Illinois Chicago（伊利诺伊大学芝加哥分校电子与计算机工程系）；Informatics Institute, Istanbul Technical University（伊斯坦布尔技术大学信息学院）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）；Department of Computer Engineering, Bilkent University（比尔肯特大学计算机工程系）
Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
Center for Applied Statistics, School of Statistics, Renmin University of China（应用统计中心，统计学院，中国人民大学）；Graduate School of Informatics, Kyoto University（信息研究生院，京都大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（社会人工智能与机器人研究所，深圳）；International Research Institute of Disaster Science, Tohoku University（灾害科学国际研究所，东北大学）
FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；College of Soil and Water Conservation, Southwest Forestry University（西南林业大学水土保持学院）；College of Civil Engineering, Southwest Forestry University（西南林业大学土木工程学院）
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Department of Electrical and Computer Engineering University of Illinois Chicago（电气与计算机工程系伊利诺伊大学芝加哥分校）；Fire and Rescue Department of Malaysia（马来西亚消防与救援部门）；Dept. of Chemical and Environmental Engineering Faculty of Engineering Universiti Putra Malaysia（化学与环境工程系乌拉尔大学马来西亚分校）；International Tropical Forestry Programme Faculty of Tropical Forestry Universiti Malaysia Sabah（国际热带林业计划热带林业系马来西亚沙巴大学）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）
