Coal-mine flood perception method based on image texture features

The existing coal-mine flood moni-toring and alarming methods have some shortcomings,such as poor adapt-ability,high false alarm and missed alarm rate,which are difficult to meet the needs of coal mine safe production. The authors put forward a method of coal-mine flood perception method based on image texture features:Setting cameras on the top or side of coal mine roadway and mining face supports,and collecting the real-time images of mining face and roadway floor. The texture features of water,coal and rock images are extracted by dual-tree complex wavelet trans-form,and the coal-mine flood image recognition model is established. The learning samples are processed by dual-tree complex wavelet transform,and the coefficients of level 1 and 2 are extracted,and the variance and expectation values are counted. The Poisson distribution model is constructed by using the corresponding variance and expectation values, and the intensity parameters of the models in all directions are estimated. After double-tree complex wavelet transform, Pearson similarity between the intensity parameter vector and the sample parameter vector of the model composed of the variance and expectation values of the first and second order coefficients is compared,and finally the classification of the samples to be tested is determined. According to the coal-mine flood image recognition model,the real-time mo-nitoring image is recognized. When the segmentation image has the coal-mine flood texture characteristics,the flood a-larm is carried out. The image of coal-mine flood simulation experiment is collected and the image database is estab-lished. The corresponding experimental program is compiled for the proposed model,and the training and experimental verification of the model are carried out. The distribution law of the coefficients statistics of water,coal and rock under the Poisson distribution in the dual-tree complex wavelet domain is studied,and the performance of the model is evalu-ated by parameterization. The feasibility of coal-mine flood perception method based on image texture features is veri-fied by experiments. Experiments show that the accuracy of mine flood recognition based on image texture features is more than 81% .