Research On The Segmentation Method Of Industrial Smoke And Dust Image For Smoke Blackness Detection

Industrial smoke is one of the main sources of air pollution.The detection of industrial smoke plays an important role in protecting the environment and reducing pollution.Smoke blackness can reflect the pollution degree of industrial smoke.Lingman blackness method is a commonly used method in the detection of smoke blackness,but this method is greatly influenced by human factors,and its efficiency is low.With the development of image recognition technology,segmentation of industrial smoke can provide a new method for smoke blackness detection.To segment smoke and dust area from background accurately is the key and difficulty of image-based method for smoke blackness detection.Aiming at this problem,this paper mainly studies the target segmentation method of industrial smoke image in daytime,which is described as follows:(1)Industrial smoke image segmentation method based on dynamic target detection and region growth.Firstly,the background is modeled by using the Gauss mixture model,and the moving area of the smoke edge is obtained by using the background difference method.Then the interference is removed by morphological processing.Finally,the smoke area is segmented by region growing algorithm in uniform space.This method is used to segment smoke images in three different scenes,and compared with other smoke segmentation methods such as mean background modeling.Experimental results show that this method is superior to other methods in recall and precision.(2)Industrial smoke image segmentation method based on target rough location and fine segmentation.In order to improve the accuracy of smoke segmentation,the edge motion region is firstly extracted,and the coarse smoke segmentation region is obtained by convex hull algorithm.On the basis of the variance between large classes,the segmentation threshold is updated secondly.Finally,segmentation experiments are carried out for different industrial soot scenarios.The experimental results show that the recall and precision of this method are improved.(3)Industrial Smoke Image Segmentation Method Based on Gauss Mixture Model with adaptive variable of step.In order to solve the problem of inaccurate smoke detection caused by updating parameters on the basis of previous fixed frame values in background modeling of traditional Gaussian mixture model,an industrial smoke image segmentation method based on adaptive variable step size Gaussian mixture model is adopted.Considering the uneven change speed of smoke,the best step size is calculated based on the change rate of entropy difference,and a model of the change rate of entropy difference and the best step size is obtained.With the change rate of entropy difference as input and the best step size as output,the network model suitable for industrial smoke image segmentation in this paper is obtained.Finally,the segmentation experiment is carried out.The experimental results show that the recall and precision of this method are higher than those of the other two methods.