Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.
YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation
School of Computer Science and Technology, Shandong Jianzhu University（计算机科学与技术学院，山东建筑大学）；International School of Information Science\&Engineering, Dalian University of Technology（信息科学与工程国际学院，大连理工大学）；Wuhan SecureScape Technology Co., Ltd（武汉SecureScape科技有限公司）；University of Glasgow, Glasgow, UK（格拉斯哥大学）
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）
FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments
University of Chester（切斯特大学）；Islamic University of Technology（伊斯兰技术大学）；Bangladesh Army University of Science and Technology（孟加拉国陆军科技大学）；School of Computer Science and Engineering The University of Aizu（会津大学计算机科学与工程学院）
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
Tecnologico de Monterrey（蒙特雷理工学院）；Universidad de las Américas Puebla（普埃布拉美洲大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；CIATEQ A.C.（CIATEQ先进技术中心）
A Low-Cost Multi-Agent System for Physical Security in Smart Buildings
School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院（ISEP/IPP））；Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD)（高级创新与发展智能工程与计算研究组（GECAD））
