Image characteristics analysis and experimental study of coal and gangue

In order to realize green,efficient and highly intelligent coal and gangue sorting based on machine visionIn this study,the distribution of two grayscale features and four texture features of a total of 420 images of coal and gangue were discussed,and the image collection of coal and gangue was carried out by simulating the illumination,water and dust environment in production to study its influence on the image features of coal and gangue.In addition,according to the four experimental factors of light intensity,humidity,degree of pulverized coal contamination and sample type,the influencing factors were quantified. The Box-Benhnken Design (BBD) experimental design theory was used to design four-factor three-level experiments. The mean value was the response index,and the significance and interaction of the influence of various factors on the gray value of the coal gangue image were studied,so as to obtain the most obvious features of distinguishing coal and gangue.The characteristic analysis shows that the grayscale characteristics of coal and gangue have a better degree of differentiation than the texture characteristics,from the point of view of the grayscale mean and peak values,6-36 W light conditions have limited influence on the grayscale mean,but make the gray peak fluctuate seriously.With the increase of spraying amount on the surface of samples,both of them decreased significantly,taking 0.08 g spraying amount as the turning point,the gray mean showed a logarithmic curve descending trend from urgent to slow.There is a linear inverse ratio relationship between the gray mean and the amount of coal powder,and the linear ratio of ash gangue is about 4-5 times that of lump coal and black gangue.The single-factor experiment shows that the gray peak is sensitive to environmental change,while the response surface method shows that the gray mean of coal and gangue is distinguishable obviously at the same level.The results are helpful to promote the application of coal and gangue separation based on machine vision,realize underground coal and gangue separation,and have reference significance for coal and rock interface identification technology.