基金项目:西安市科技计划项目(2017079CG/RC042(XAKD001); 西安科技大学科研培育基金(201744); 西安科技大学博士启动金(6310116057)
通信作者:李 娜(1981-),女,陕西蒲城人,博士,讲师,E-mail:mamawork@sina.com
Contact fire detectors have limitations in large space and outdoor scenes,so combining video surveillance system with digital image processing technology to conduct fire detection and early warning has become a new research direction in the field of fire detection.Aiming at the selection of smoke recognition algorithms in different scenarios,this paper studies the application of color statistical features and wavelet transform in smoke image recognition,and analyzes its applicability through the specific process of the algorithm.Fire smoke image contains rich color,texture and other characteristic parameters.In the statistical method of color feature,smoke image is converted into binary image,and the RGB color feature of smoke is counted by digital image processing method.Then,the relationship between color distance and human vision is analyzed to extract smoke feature,and then the purpose of identifying whether there is fire smoke and early warning is achieved.Texture of fume background image is blurring,which is regarded as signal weakening,and this is used to extract target foreground in the method of wavelet transform.Mean background is calculated by using continuous target image.By comparing with mean background,the connected area of image signal attenuation is captured and smoke image is obtained.Through experiments,the performance indicators of color feature statistics and wavelet transform methods in fire smoke recognition are obtained from image requirements,smoke objects,time complexity and suitable places.The results will guide the selection of smoke image recognition methods in different scenarios.