Research on image recognition method of coal gangue based on MPSO-BP neural network
To deal with the problems of low contrast,complex environment and difficult boundary recognition of coal gangue images,a coal gangue recognition method based on improved particle swarm optimization algorithm and neural network was proposed.Firstly,the coal gangue image was preprocessed and the texture features were extracted.Secondly,the backpropagation neural network was optimized using improved particle swarm optimization algorithm by combining texture features and gray mean.Finally,transfer learning and convo-lutional neural network models were introduced.The results show that the neural network combined with gray mean and texture features is more effective than the traditional method in identifying coal gangue,and the recognition time is shorter,only 1 980 s.After the intro-duction of transfer learning and convolutional neural network,the recognition accuracy of the extended database is improved by 2.47％,1.47％and 2.60％,respectively.The performance accuracy of the improved model is up to 0.95.Compared with the traditional meth-ods,the research method is improved in both time and recognition accuracy,which provides theoretical and practical value for online coal image quality detection.
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