Open Access

ARTICLE

Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

by Fangming Bi, Xuanyi Fu, Wei Chen, Weidong Fang, Xuzhi Miao, Biruk Assefa

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.

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Citations