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Research Article

Multiple factors influence coal and gangue image recognition method and experimental research based on deep learning

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Pages 1411-1427 | Received 26 May 2022, Accepted 18 Aug 2022, Published online: 05 Sep 2022

References

  • Bai, T. B., J. L. Gao, J. W. Yang, and D. C. Yang. 2021. A study on railway surface defects detection based on machine vision. Entropy 23 (11):1437. doi:10.3390/e23111437.
  • Cao, X. G., S. Y. Liu, P. Wang, G. Xu, and X. D. Wu. 2022. Research on coal gangue identification and positioning system based on coal-gangue sorting robot. Coal Science and Technology 50 (01):237–46.
  • Dou, D. Y., W. Z. Wu, J. G. Yang, and Y. Zhang. 2019. Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM. Powder Technology 356:1024–28. doi:10.1016/j.powtec.2019.09.007.
  • Eshaq, R. M. A., E. Hu, M. G. Li, and M. S. A. 2020. Separation between coal and gangue based on infrared radiation and visual extraction of the YCbCr color space. IEEE Access 8:55204–20. doi:10.1109/ACCESS.2020.2981534.
  • Fu, H. X., G. Q. Song, and Y. C. Wang. 2021. Improved YOLOv4 marine target detection combined with CBAM. Symmetry 13 (4):623. doi:10.3390/sym13040623.
  • Guo, Y. C., X. Wang, L. He, and P. Z. 2022. Research on coal ang gangue recognition method based on TW-RN optimized CNN. Coal Science and Technology 50 (01):228–36.
  • He, M., P. P. Wang, and H. H. Jiang. 2012. Recognition of coal and stone based on SVM and texture. Computer Engineering and Design 33 (03):1117–21.
  • Hong, H. C. 2018. Study of selection algorithm of waste rock from coal bulk based on machine vision. Huaqiao University.
  • Huo, P., H. L. Zeng, and K. Y. Huo. 2015. Research on density identification system of coal and refuse based on image processing technology. Coal Preparation Technology 02:69–73.
  • Lei, S. W., X. M. Xao, and M. Zhang. 2021. Research on coal and gangue identification method based on improved YOLOv3. Mining Safety & Environmental Protection 48 (03):50–55.
  • Li, Y. 2020. Research on coal gangue detection based on deep learning. Xi’ an: an University of Science and Technology.
  • Li, M., Y. Duan, X. G. Cao, C. Y. Liu, K. K. Sun, and H. Liu. 2020. Image identification method and system for coal and gangue sorting robot. Journal of China Coal Society 45 (10):3636–44.
  • Li, M., Y. Duan, X. L. He, and M. L. Yang. 2020. Image positioning and identification method and system for coal and gangue sorting robot. International Journal of Coal Preparation and Utilization 42 (6).
  • Li, M., X. L. He, M. L. Yang, and Y. Duan. 2022. Experimental study on the influence of external moisture on image features of coal and gangue. Coal Science and Technology. 1–8
  • Li, M., and K. K. Sun. 2018. An image recognition approach for coal and gangue used in pick-up robot. IEEE International Conference on Real-Time Computing and Robotics (RCAR), Kandima, Maldives.
  • Li, B., C. Wang, J. Wu, J. C. Liu, L. J. Tong, and Z. Y. Guo. 2021. Surface defect detection of aeroengine components basedon improved YOLOv4 algorithm. Laser & Optoelectronics Progress 58 (14):414–23.
  • Li, D. Y., G. F. Wang, Y. Zhang, S. Wang, J. Chen, H. Zhao, Y. Chong, H. Wu, Y. Yang, and J. Shen. 2021. A coarse-refine segmentation network for COVID-19 CT images. IET Image Processing 16 (2):333–43. doi:10.1049/ipr2.12278.
  • Liao, Y. Y. 2015. Recognition of coal and stone based on BP. Industrial Control Computer 28 (7):119–20+122.
  • Liu, K., X. Zhang, and Y. Q. Chen. 2018. Extraction of coal and gangue geometric features with multifractal detrending fluctuation analysis. Applied Sciences 8 (3).
  • Mi, Q., Y. Xu, B. Liu, and Y. J. Xu. 2017. Extraction method of texture fea-ture of images of coal and gangue. Industry & Mine Automation 43 (05):26–30.
  • Pang, S. Z., B. Li, X. W. Wang, L. Y. Wang, X. Y. Gao, Y. Song, and E. F. Ding. 2021. Design and experimental research of coal and gangue recognition system based on machine vision. Coal Engineering 53 (02):141–46.
  • Pu, Y. Y., D. B. Apel, A. Szmigiel, and J. Chen. 2019. Image Recognition of coal and coal gangue using a convolutional neural network and transfer learning. Energies 12 (9):1735. doi:10.3390/en12091735.
  • Rao, Z. Y., J. T. Wu, and M. Li. 2020. Coal-Gangue image classification method. Industry and Mine Automation 2020 (03):69–73.
  • Shan, P. F., H. Q. Sun, X. P. Lai, X. P. Zhu, J. H. Yang, and J. M. Gao. 2022. Identification method on mixed and release state of coal-gangue masses of fully mechanized caving based on improved Faster R-CNN. Journal of China Coal Society 47 (03):1382–94.
  • Shang, D. Y., L. Zhang, Y. Q. Niu, and X. Fan. 2022. Design and key technology analysis of coal - gangue sorting robot, 1–7. Coal Science and Technology.
  • Shen, N., D. Y. Dou, C. Yang, and Y. Zhang. 2019. Research on multi-condition identification of Gangue based on machine vision. Coal Engineering 01:120–25.
  • Su, L. L., X. G. Cao, H. W. Ma, and Y. Li. 2018. Research on coal gangue identification by using convolutional neural network. 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi’an, 810–14.
  • Su, B. L., B. Chen, J. F. He, N. L. H, Y. Q. He, Q. Q. He, and K. Li. 2011. Research on the automatic identification technology of the coal and gangue based on density histogram. Clean Coal Technology 17 (06):96–98.
  • Tan, C. C., and J. M. Yang. 2017. Research on extraction of image gray information and texture features of coal and gangue image. Industry and Mine Automation 43 (04):27–31.
  • Tao, H., and Y. Jiang. 2021. Application research of improved YOLOv4 in remote sensing aircraft target detection. Computer Engineering and Applications 57 (12):224–30.
  • Tripathy, D. P., and K. R. Reddy. 2017. Novel methods for separation of gangue from limestone and coal using multispectral and joint color-texture features. Journal of the Institution of Engineers (India): Series D 98 (1):109–17. doi:10.1007/s40033-015-0106-4.
  • Wang, P., X. G. Cao, H. W. Ma, X. D. Wu, and J. Xia. 2020. Dynamic target steady and accurate grasping algorithm of gangue sorting robot based on cosine theorem-PID. Journal of China Coal Society 45 (12):4240–47.
  • Wang, G. F., Y. B. Du, H. W. Ren, J. D. Fan, and Q. X. Wu. 2020. Top level design and practice of smart coal mines. Journal of China Coal Society 45 (06):1909–24.
  • Wang, B. J., H. X. Huang, D. Y. Dou, and Z. Y. Qiu. 2021. Detection of coal content in gangue via image analysis and particle swarm optimization-support vector machine. International Journal of Coal Preparation and Utilization 2021:1–10.
  • Wang, J. C., L. H. Li, and S. L. Yang. 2018. Experimental study on gray and texture features extraction of coal and gangue image under different illuminance. Journal of China Coal Society 43 (11):3051–61.
  • Wang, W. D., Z. Q. Lv, H. G. Lu, S. Wang, H. Wei, T. Zhang, X. Zhu, and Z. Lu. 2018. Research on methods to differentiate coal and gangue using image processing and a support vector machine. International Journal of Coal Preparation and Utilization 41 (1):1–14. doi:10.1080/19392699.2018.1483353.
  • Wu, G. P., X. G. Liang, J. L. Hu, and X. D. Ge. 2019. Experimental study on analysis of coal gangue by image processing and support vector machine. Information Technology 1:97–102,107.
  • Wu, K. X., and J. Song. 2016. Automatic coal-gangue identification based on gray level co-occurrence matrix. Coal Engineering 48 (02):98–101.
  • Xie, B. H., S. Yuan, and D. L. 2022. Gong. detection of blocked pedestrians based on RDB-YOLOv4 in the mine. Computer Engineering and Applications 58 (05):200–07.
  • Xu, J., and F. W. Wang. 2011. Study of automatic separation system of coal and gangue by ir image recognition technology. Advances in Automation and Robotics 2:87–92.
  • Xue, G. H., X. Y. Li, X. L. Qian, and Y. F. Zhang. 2020. Coal-Gangue image recognition infully-mechanized caving face based on random forest. Industry and Mine Automation 46 (05):57–62.
  • Yang, L., J. C. Luo, X. W. Song, M. L. Li, P. F. Wen, and Z. X. Xiong. 2021. Robust vehicle speed measurement based on feature information fusion for vehicle multi-characteristic detection. Entropy 23 (7):910. doi:10.3390/e23070910.
  • Yu, G. F., S. Q. Zou, and C. Qin. 2012. Application research of image gray information in automatic separation of coal and gangue. Industry and Mine Automation 38 (02):36–39.

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