A Statistical Image Feature-Based Deep Belief Network for Fire Detection
Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this pape...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5554316 |
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| author | Dali Sheng Jinlian Deng Wei Zhang Jie Cai Weisheng Zhao Jiawei Xiang |
| author_facet | Dali Sheng Jinlian Deng Wei Zhang Jie Cai Weisheng Zhao Jiawei Xiang |
| author_sort | Dali Sheng |
| collection | DOAJ |
| description | Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy. |
| format | Article |
| id | doaj-art-c887022dfc6644248e0696b4fa3defd8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c887022dfc6644248e0696b4fa3defd82025-02-03T06:12:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55543165554316A Statistical Image Feature-Based Deep Belief Network for Fire DetectionDali Sheng0Jinlian Deng1Wei Zhang2Jie Cai3Weisheng Zhao4Jiawei Xiang5Department of Police Command and Tactics, Zhejiang Police College, Hangzhou 310053, ChinaDepartment of Mechanical Engineering, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, ChinaDepartment of Mechanical Engineering, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, ChinaDepartment of Mechanical Engineering, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, ChinaDepartment of Mechanical Engineering, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, ChinaCollege of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaDetecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy.http://dx.doi.org/10.1155/2021/5554316 |
| spellingShingle | Dali Sheng Jinlian Deng Wei Zhang Jie Cai Weisheng Zhao Jiawei Xiang A Statistical Image Feature-Based Deep Belief Network for Fire Detection Complexity |
| title | A Statistical Image Feature-Based Deep Belief Network for Fire Detection |
| title_full | A Statistical Image Feature-Based Deep Belief Network for Fire Detection |
| title_fullStr | A Statistical Image Feature-Based Deep Belief Network for Fire Detection |
| title_full_unstemmed | A Statistical Image Feature-Based Deep Belief Network for Fire Detection |
| title_short | A Statistical Image Feature-Based Deep Belief Network for Fire Detection |
| title_sort | statistical image feature based deep belief network for fire detection |
| url | http://dx.doi.org/10.1155/2021/5554316 |
| work_keys_str_mv | AT dalisheng astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jinliandeng astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT weizhang astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jiecai astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT weishengzhao astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jiaweixiang astatisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT dalisheng statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jinliandeng statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT weizhang statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jiecai statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT weishengzhao statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection AT jiaweixiang statisticalimagefeaturebaseddeepbeliefnetworkforfiredetection |