Machine vision recognition method and optimization for intelligent separation of coal and gangue

The key to separate coal from gangue intelligently is the image recognition of coal and gangue,and the deep convolutional neural networks can solve this problem. The authors have collected a large number of coal and gangue images at the transportation belt during the production,taken as training samples,and built some coal and gangue im- age recognition models based on classical deep learning networks ( e. g. ResNet) and lightweight deep learning net- works (e. g. SqueezeNet). Also the authors prune some models based on the similarity of feature ex-tracted by different convolutional kernels of these models,and the similarity is measured by clustering results of k-means++. Recognition accuracy,model size and operation complexity of each model is compared. Finally,the authors have visualized heat- maps of class activation in different images to analyze the recognition basis of coal and gangue during the production by CNN. The results show that most existing CNN can be used to differentiate coal and gangue effectively,but the com- plexity of networks has a great impact on the accuracy. The coal and gangue recognition model based on model pruning can accurately capture the surface differences between coal and gangue due to their different hardness,and the reflec- tion generated by vitelline composition at the truncated surface of coal can be used as a reliable basis for identifying coal. The calculation amount and model size of this model are reduced by 10 times,and the recognition accuracy is in- creased by 17. 8% . This method can save some computing and storage resources on the premise of ensuring accuracy, and the performance is significantly better than the conventional network model.