@Article{cmc.2020.06258,
AUTHOR = {Fangming Bi, Xuanyi Fu, Wei Chen, Weidong Fang, Xuzhi Miao, Biruk Assefa},
TITLE = {Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {1},
PAGES = {199--216},
URL = {http://www.techscience.com/cmc/v62n1/38107},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06258}
}