Research On Online Detection Method Of Coal Content Of Massive Gangue Based On Machine Vision

The coal content of gangue is an important technical assessment index in the field of coal sorting,which reflects the performance of the sorting equipment and the level of operators.At present,this indicator cannot be detected online.For man-made detection methods,a lot of manpower and material resources are consumed,and detection takes too long to be unfavorable for on-site control,which severely restricts the development of intelligent coal preparation.Therefore,it is extremely urgent to realize real-time detection of the coal content of gangue through a new method.The rapid development of machine vision technology has made it widely used in the field of coal washing.Based on the existing research,given the current situation that the coal content of gangue has not yet been detected online,a machine vision-based online detection method for the coal content of gangue is explored.Starting from the theoretical calculation of the rate of coal containing waste rock,through image processing technology,coal gangue recognition model,coal gangue volume prediction model,etc.,an online detection method of gangue coal content based on machine vision is explored.In the process of using image processing technology,first,a multi-scale edge detection algorithm based on Gaussian function and the Hessian matrix is used for image segmentation;also considering the lack of segmentation area caused by occlusion in stacked Coal and gangue images,it is complemented by convex hull algorithm Missing regions are proposed to reduce the error in extracting dimensional features and shape features.For the Coal and gangue recognition model,12 image features that can be used for coal and gangue recognition are selected based on existing research results.The extracted features are combined into a training sample set to build a decision tree recognition model.The experimental results show that coal and gangue can be effectively identified by the decision tree algorithm.Based on this,through ensemble learning,a Coal and gangue recognition model based on the Ada Boost-decision tree algorithm is established to further improve the accuracy of recognition.The 10-fold cross-validation classification error rate of the recognition model is at least 3.29%.In the volume prediction of coal and gangue,based on the relationship between shape and volume,a volume regression prediction model based on shape classification is proposed.First,according to the shape characteristics of coal and gangue,through K-Means++ clustering,the feature value of each shape feature is initially discretized,and then according to the discrete shape feature,the classification of coal or gangue based on shape feature is realized through K-Modes clustering.Based on the existing research on the volume prediction regression model of lump coal,the transformation of two-dimensional size features into three-dimensional volume features as the independent variable of the volume prediction regression model is proposed.After that,all the independent variables that can be used as the volume prediction regression model are listed.The independent variables of the optimal regression model are found through the full subset regression and combined with the regression t-test,5 independent variables that can be used for the volume prediction regression model are obtained,and finally,Robust regression based on bisquare weighting method is used to reduce the influence of abnormal samples.According to the shape classification results of coal or gangue,a volume regression model is established for different shape categories.The average relative error of the model is about 10%,indicating that the volume model of coal and gangue can be predicted effectively.Based on the research of image processing technology,Coal and gangue recognition model,and Coal and gangue volume prediction model,an online detection method of gangue coal content based on machine vision is proposed,and the effect of this method is tested by laboratory simulation.In the laboratory simulation process,the density distribution of coal and gangue samples was studied.On the basis that it did not obey the normal distribution,the Bootstrap method was used to solve the density mean and its confidence interval,and the density mean was used to calculate the coal and gangue.The quality error is about 2% and 4% respectively.Given the problem that the predicted value of the coal content of gangue obtained in the laboratory simulation process is much smaller than the actual value,after combing the prediction process,it is found that the divided area is generally larger than the actual area.To reduce the error caused by the division,compensation is added.The calculation formula for the coal content of the gangue in the final laboratory simulation is proposed.The average relative error of the prediction of the coal content of the gangue in the final laboratory simulation is 14.81%,which is the first method to realize the online detection of the gangue coal content,and the prediction accuracy is still satisfactory.