Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images

Abstract

Recognition and separation of Coal/Gangue are important phases in the coal industries for many aspects. This paper addressed the topic of Coal/Gangue recognition and built a new model called (CGR-CNN) based on Convolutional Neural network (CNN) and using thermal images as standard images for Coal/Gangue recognition. The CGR-CNN model has been developed, augmentation principle has been applied in order to increase the dataset and the best experimental results have been achieved (99.36%) learning accuracy and (95.09%) validation accuracy, in the prediction phase (160) new images of coal and gangue (80 for both) have been tested to measure the efficiency of the work, the prediction result comes with (100%) for coal recognition accuracy and (97.5%) gangue recognition accuracy giving an overall prediction accuracy (98.75%).

Keywords

• Coal
• coal gangue
• convolutional neural network
• object classification
• dataset augmentation
• thermal images