dc.contributor.other | Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University | |
dc.creator | Wu, Hao | |
dc.creator | Xiong, Hao | |
dc.creator | Wang, Chengjiang | |
dc.creator | Du, Linhan | |
dc.creator | Zhang, Jiajun | |
dc.creator | Zhao, Jinsong | |
dc.date.accessioned | 2021-06-11T18:56:01Z | |
dc.date.available | 2021-06-11T18:56:01Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/193499 | |
dc.description | Presentation | en |
dc.description.abstract | Fire that is one of the most serious accidents in chemical factories, may lead to considerable product losses, equipment damages and casualties. With the rapid development of computer vision technology, intelligent fire detection has been proposed and applied in various scenarios. This paper presents a new intelligent video fire detection approach based on object detection technology using convolutional neural networks (CNN). First, a CNN model is trained for the fire detection task which is framed as a regression problem to predict bounding boxes and associated probabilities. In the application phase, videos from surveillance cameras are detected frame by frame. Once fire appears in the current frame, the model will output the coordinates of the fire region. Simultaneously, the frame where the fire region is localized will be immediately sent to safety supervisors as a fire alarm. This will help detect fire at the early stage, prevent fire spreading and improve the emergency response. | en |
dc.format.extent | 5 pages | en |
dc.language | eng | |
dc.publisher | Mary Kay O'Connor Process Safety Center | |
dc.relation.ispartof | Mary K O'Connor Process Safety Symposium. Proceedings 2018. | en |
dc.rights | IN COPYRIGHT - EDUCATIONAL USE PERMITTED | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC-EDU/1.0/ | |
dc.subject | Fire Detection Approach | en |
dc.title | An intelligent video fire detection approach based on object detection technology | en |
dc.type.genre | Papers | en |
dc.format.digitalOrigin | born digital | en |
dc.publisher.digital | Texas &M University. Libraries | |