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The separation of coal and gangue is an important aspect of coal production, which can improve coal quality, save energy, reduce consumption, and enable the rational use of resources. At present, manual selection remains widely implemented, but the working environment is harsh, labour intensity is high, and efficiency is low. With the continual developments in artificial intelligence and image technology, in this paper, a new method is proposed to separate gangue from coal based on image processing, the visual differences between the coal and gangue are analysed, and the supplementary texture is extracted based on morphology. Pre-treatments of coal and gangue images, including filtering, sharpening, and segmentation, are also introduced. Owing to the differences in physicochemical properties, coal and gangue exhibit different visual traces in the process of mining and transportation, but the traces appear to be irregular. Thus, the morphological method is applied to seek the region of interest where the coal or gangue body is exposed, and then the supplementary texture is extracted to establish the coal and gangue classifier. The effect of this classifier is evaluated and compared to the traditional texture classifier, and it is found that the recognition accuracy is significantly improved without special treatment (wash or blow).

Separation of gangue from coal based on supplementary texture by morphology