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Illuminance is a key factor in optical sorting in underground coal mining, since it affects the image grayscale and texture features, especially for objects that are sensitive to light, such as bright coal. Appropriate illuminance is beneficial for improving the accuracy of image-based rock mixing ratio estimation, which is essential for optical sorting in underground coal mining. In this study, an image acquisition system with controllable illuminance was established. Groups of images were obtained under six illuminance conditions: 3180, 10780, 17730, 24200, 30700, and 35600 lx. MATLAB code was developed to segment an image and extract image features from each segmented subregion. The relations between illuminance and the image features for bright coal were quantified. The differences in the features between bright coal and sandstone were discussed. Two theoretical models, including area model and volume model, were used for estimating rock mixing ratio of the digital image. Our main conclusions are as follows. In general, the image features of bright coal blocks are different from those of sandstone blocks. There were significant differences between bright coal and sandstone for the variance under 24200 lx, the kurtosis under 10780 lx, and the fractal dimension under 3180 lx. Therefore, the proper illuminance is necessary for accurately determining the image features of bright coal and could even moderately increase the differences in the image features between bright coal and sandstone. Rock mixing ratio obtained by the area model is in better agreement with the manual results than volume model in this study. This research is of great significance as it presents a novel idea for improving the accuracy of optical sorting for underground mining.

Image-based rock mixing ratio estimation by using illuminance analysis in underground mining