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A platform for research: civil engineering, architecture and urbanism
An Efficient Fire Detection System Using Support Vector Machine and Deep Neural Network
https://orcid.org/http://orcid.org/0000-0003-2937-4535
https://orcid.org/http://orcid.org/0000-0003-4412-9765
https://orcid.org/http://orcid.org/0000-0002-9013-8454
https://orcid.org/http://orcid.org/0000-0002-3742-3037
https://orcid.org/http://orcid.org/0000-0001-5811-9555
Fire is a dangerous disaster. Uncontrollable fire can cause massive destruction to life and property. Hence fire detection and alarming is a crucial and ever-demanded topic. A fire alarm must be functionally capable and reliable. This paper mainly focuses on a real-time system for fire detection. Videos acquired from CCTVs or webcams are converted to a sequence of images which are then fed to the classifier. Upon detecting fire from the images extracted, an alert is sent to the authorities concerned. Support vector machine (SVM) and deep neural network are used to develop the proposed fire detection system. Both the algorithms are employed to build classification models. Their performances are then compared and the model which gives better accuracy is selected.
An Efficient Fire Detection System Using Support Vector Machine and Deep Neural Network
https://orcid.org/http://orcid.org/0000-0003-2937-4535
https://orcid.org/http://orcid.org/0000-0003-4412-9765
https://orcid.org/http://orcid.org/0000-0002-9013-8454
https://orcid.org/http://orcid.org/0000-0002-3742-3037
https://orcid.org/http://orcid.org/0000-0001-5811-9555
Fire is a dangerous disaster. Uncontrollable fire can cause massive destruction to life and property. Hence fire detection and alarming is a crucial and ever-demanded topic. A fire alarm must be functionally capable and reliable. This paper mainly focuses on a real-time system for fire detection. Videos acquired from CCTVs or webcams are converted to a sequence of images which are then fed to the classifier. Upon detecting fire from the images extracted, an alert is sent to the authorities concerned. Support vector machine (SVM) and deep neural network are used to develop the proposed fire detection system. Both the algorithms are employed to build classification models. Their performances are then compared and the model which gives better accuracy is selected.
An Efficient Fire Detection System Using Support Vector Machine and Deep Neural Network
https://orcid.org/http://orcid.org/0000-0003-2937-4535
https://orcid.org/http://orcid.org/0000-0003-4412-9765
https://orcid.org/http://orcid.org/0000-0002-9013-8454
https://orcid.org/http://orcid.org/0000-0002-3742-3037
https://orcid.org/http://orcid.org/0000-0001-5811-9555
Fire is a dangerous disaster. Uncontrollable fire can cause massive destruction to life and property. Hence fire detection and alarming is a crucial and ever-demanded topic. A fire alarm must be functionally capable and reliable. This paper mainly focuses on a real-time system for fire detection. Videos acquired from CCTVs or webcams are converted to a sequence of images which are then fed to the classifier. Upon detecting fire from the images extracted, an alert is sent to the authorities concerned. Support vector machine (SVM) and deep neural network are used to develop the proposed fire detection system. Both the algorithms are employed to build classification models. Their performances are then compared and the model which gives better accuracy is selected.
An Efficient Fire Detection System Using Support Vector Machine and Deep Neural Network
Lecture Notes in Civil Engineering
Shukla, Sanjay Kumar (editor) / Chandrasekaran, Srinivasan (editor) / Das, Bibhuti Bhusan (editor) / Kolathayar, Sreevalsa (editor) / Venugopal, Archana (author) / Justin, Febi (author) / Santhosh, Linju (author) / Binny, Riya (author) / Resmi, NG (author)
2020-10-14
8 pages
Article/Chapter (Book)
Electronic Resource
English
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