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Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM -SVM

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  • Murad S. Alfarzaeai

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China
    School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Eryi Hu

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China)

  • Wang Peng

    (Information Institute, Ministry of Emergency Management of the People’s Republic of China, Beijing 100029, China)

  • Niu Qiang

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Maged M. A. Alkainaeai

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

Abstract

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 ( E x M ) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives ( E x M ) 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 E x M -SVM in coal gangue classification tasks.

Suggested Citation

  • Murad S. Alfarzaeai & Eryi Hu & Wang Peng & Niu Qiang & Maged M. A. Alkainaeai, 2023. "Coal Gangue Classification Based on the Feature Extraction of the Volume Visual Perception ExM -SVM," Energies, MDPI, vol. 16(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2064-:d:1074737
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    References listed on IDEAS

    as
    1. Rong Gao & Zhaoyun Sun & Wei Li & Lili Pei & Yuanjiao Hu & Liyang Xiao, 2020. "Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks," Energies, MDPI, vol. 13(4), pages 1-13, February.
    2. Yuanyuan Pu & Derek B. Apel & Alicja Szmigiel & Jie Chen, 2019. "Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning," Energies, MDPI, vol. 12(9), pages 1-11, May.
    3. Bing Wang & Chao-Qun Cui & Yi-Xin Zhao & Bo Yang & Qing-Zhou Yang, 2019. "Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1345-1364, December.
    Full references (including those not matched with items on IDEAS)

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