Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes. We analyze the infrared images of coal and gangue; then extract the appropriate texture features from the infrared images in order to develop an accurate classification system by using support vector machine (SVM). The method applied in this work essentially depends on feature extraction of images. The statistical features based on gray level information (GLI), grey-level cooccurrence matrix (GLCM) and visual features are executed. Thus, we suggest preparation steps to obtain one select feature before importing the data into the SVM classifier, and this approach is adopted as the fundamental basis for our work. We exploit only one feature of the infrared image, namely, Cb, which is extracted from the YCbCr color space, and then compute the mean value of Cb after heating and capturing the photos for the coal and gangue samples. The proposed method achieves a high classification accuracy 97.83 % by using Gaussian-SVM.
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%).
Computer-vision-based separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like density. This study achieves the classification of coal and gangue based on their mass, volume, and weight. A dataset of volume, weight and 3_side images is collected. By using 3_side images of coal gangue, the visual perception value of the volume is extracted (ExM) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives (ExM) and the weight to perform the coal gangue classification. The proposed system eliminates computer vision problems like light intensity, dust, and heterogeneous coal sources. The proposed model was tested with a collected dataset and achieved high recognition accuracy (KNN 100%, Linear SVM 100%, RBF SVM 100%, Gaussian Process 100%, Decision Tree 98%, Random Forest 100%, MLP 100%, AdaBosst 100%, Naive Bayes 98%, and QDA 99%). A cross-validation test has been done to verify the generalization ability. The results also demonstrate high classification accuracy (KNN 96%, Linear SVM 100%, RBF SVM 96%, Gaussian Process 96%, Decision Tree 99%, Random Forest 99%, MLP 100%, AdaBosst 99%, Naive Bayes 99%, and QDA 99%). The results show the high ability of the proposed technique ExM-SVM in coal gangue classification tasks.
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