Abstract
Automatic coal-rock recognition is one of the critical technologies for intelligent coal mining and processing. Most existing coal-rock recognition methods have some defects, such as unsatisfactory performance and low robustness. To solve these problems, and taking distinctive visual features of coal and rock into consideration, the multi-scale feature fusion coal-rock recognition (MFFCRR) model based on a multi-scale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN) is proposed in this paper. Firstly, the multi-scale CLBP features are extracted from coal-rock image samples in the Texture Feature Extraction (TFE) sub-model, which represents texture information of the coal-rock image. Secondly, the high-level deep features are extracted from coal-rock image samples in the Deep Feature Extraction (DFE) sub-model, which represents macroscopic information of the coal-rock image. The texture information and macroscopic information are acquired based on information theory. Thirdly, the multi-scale feature vector is generated by fusing the multi-scale CLBP feature vector and deep feature vector. Finally, multi-scale feature vectors are input to the nearest neighbor classifier with the chi-square distance to realize coal-rock recognition. Experimental results show the coal-rock image recognition accuracy of the proposed MFFCRR model reaches 97.9167%, which increased by 2%–3% compared with state-of-the-art coal-rock recognition methods.
Highlights
Coal is a precious natural resource all over the world [1]
The Multi-scale Feature Fusion Coal-Rock Recognition (MFFCRR) model based on Completed Local Binary Pattern (CLBP) and Convolution Neural Network (CNN) is proposed to extract and fuse the texture and deep features of coal-rock images for coal-rock recognition
The multi-scale feature extraction partpart includes two paralleled steps:steps: extracting the texture features features and deep features, which are extracted in the sub-model based on and deep features, which are extracted in the Texture Feature Extraction (TFE) sub-model based on CLBP and the Deep Feature Extraction (DFE) sub-model sub-model on CNN,Firstly, respectively
Summary
Coal is a precious natural resource all over the world [1]. China has comparatively abundant coal resources; the nation is and will continue to be the largest coal consumer and producer in the foreseeable future [2,3]. In [9], the authors proposed the coal-rock image recognition method based on dictionary learning, which is used to extract features from coal-rock images. Some methods based on dictionary learning can fully extract the features of coal-rock images and more accurately represent coal-rock images This high-quality feature representation requires sufficient samples in the training process. The descriptor CLBP can be directly utilized to extract the local texture features from the coal-rock images, but the recognition accuracy is not high enough. The Multi-scale Feature Fusion Coal-Rock Recognition (MFFCRR) model based on CLBP and CNN is proposed to extract and fuse the texture and deep features of coal-rock images for coal-rock recognition.
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