Open Access
ARTICLE
Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM
Fangming Bi1, 2, Xuanyi Fu1, 2, Wei Chen1, 2, 3, *, Weidong Fang4, Xuzhi Miao1, 2, Biruk Assefa1, 5
1 College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.
2 College of Computer Science and Technology, Mine Digitization Engineering Research Center of the
Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.
3 College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054,
China.
4 Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Micro-System and
Information Technology, Chinese Academy of Sciences, Shanghai, 201899, China.
5 Infromation Communication Technology Department, Wollo University, Dessie Ethiopia, P.o Box 1145, Ethiopia.
* Corresponding Author: Wei Chen. Email: .
Computers, Materials & Continua 2020, 62(1), 199-216. https://doi.org/10.32604/cmc.2020.06258
Abstract
Aiming at the defects of the traditional fire detection methods, which are
caused by false positives and false negatives in large space buildings, a fire identification
detection method based on video images is proposed. The algorithm first uses the hybrid
Gaussian background modeling method and the RGB color model to perform fire
prejudgment on the video image, which can eliminate most non-fire interferences.
Secondly, the traditional regional growth algorithm is improved and the fire image
segmentation effect is effectively improved. Then, based on the segmented image, the
dynamic and static features of the fire flame are further analyzed and extracted in the area
of the suspected fire flame. Finally, the dynamic features of the extracted fire flame
images were fused and classified by improved fruit fly optimization support vector
machine, and the recognition results were obtained. The video-based fire detection
method proposed in this paper greatly improves the accuracy of fire detection and is
suitable for fire detection and identification in large space scenarios.
Keywords
Cite This Article
F. Bi, X. Fu, W. Chen, W. Fang, X. Miao
et al., "Fire detection method based on improved fruit fly optimization-based svm,"
Computers, Materials & Continua, vol. 62, no.1, pp. 199–216, 2020. https://doi.org/10.32604/cmc.2020.06258
Citations