IMAGE RECOGNITION OF COAL-GANGUE BASED ON TRANSFER LEARNING

The traditional coal-gangue image recognition algorithms need to extract and filter specific image features, which is time-consuming and labor-intensive, and the reconstruction and training of convolutional neural networks require huge data sets and high-configuration hardware equipment. This paper proposes recognition methods for coal-gangue images based on the transfer learning. Combined with VGG16 convolution basis for extracting coal-gangue images features and machine learning algorithm, the effectiveness of VGG16 convolution basis feature extraction was verified. The migration of network model was realized through feature extraction and model fine-tuning. This paper constructed two customized dense connection classifiers, and obtained two classification models. The simulation results show that the accuracy rates are 96.30% and 98.15% respectively. The coalgangue identification models obtained by the transfer learning are effective, and they are able to identify coal and gangue images quickly and accurately.