Abstract
Coal gangue is a kind of industrial waste in the coal mine preparation process. Compared to conventional manual or machine-based separation technology, vision-based methods and robotic grasping are superior in cost and maintenance. However, the existing methods may have a poor recognition accuracy problem in diverse environments since coals and gangues’ apparent features can be unreliable. This paper analyzes the current methods and proposes a vision-based coal and gangue recognition model LTC-Net for separation systems. The preprocessed full-scale images are divided into n × n local texture images since coals and gangues differ more on a smaller scale, enabling the model to overcome the influence of characteristics that tend to change with the environment. A VGG16-based model is trained to classify the local texture images through a voting classifier. Prediction is given by a threshold. Experiments based on multi-environment datasets show higher accuracy and stability of our method compared to existing methods. The effect of n and t is also discussed.
Highlights
Coal gangue, or gangue, is a kind of black or gray solid waste discharged in coal mining and coal washing
Tripathy [15] extracted color and texture features, respectively, for the latter classification. These works are in the two-step structure, which performs classification with trained models like support vector machine (SVM) or neural networks on features extracted from original images
Gray Level Co-occurrence Matrix (GLCM) + SVM: To four key parameters in GLCM: entropy, energy, inverse different moment, and contrast, respectively, are computed for each of Dataset1_train images, and an SVM model is trained to perform classification based on the four parameters
Summary
Gangue, is a kind of black or gray solid waste discharged in coal mining and coal washing. While for one-step or direct methods, classifiers are trained directly to recognize images instead of manually-extracted parameters. Liang’s work [9] was a representation of two-step methods It extracted eight characteristic parameters from the Gray Level Co-occurrence Matrix (GLCM) of the original full-scale images and trained an SVM model or a B.P. neural network as a prediction boundary. Gray information was extracted to recognize coal and gangue images using partial grayscale compression with extended coexistence. Tripathy [15] extracted color and texture features, respectively, for the latter classification These works are in the two-step structure, which performs classification with trained models like SVM or neural networks on features extracted from original images. This paper proposes a Local Texture Classification Network (LTC-Net) method to improve the classification performance in different working conditions.
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