Review: Identifikasi Batubara dan Gangue Menggunakan Machine Learning dan Deep Learning
DOI:
https://doi.org/10.31294/imtechno.v5i1.3013Keywords:
identifikasi batubara dan gangue, machine learning, deep learningAbstract
Abstrak - Pemisahan gangue dari batubara adalah hal yang cukup penting dalam dunia industri pertambangan batubara. Berbagai upaya telah dilakukan oleh para peneliti dalam mencari cara yang lebih efektif dan efisien untuk identifikasi gangue dan batubara. Diantara metode yang cukup banyak diminati oleh para peneliti dalam satu dekade terkahir ini adalah penggunaan teknologi machine learning dan deep learning. Studi review ini bertujuan untuk melihat tren penelitian terkait klasifikasi batubara dan gangue yang menggunakan metode machine learning dan deep learning. Studi ini juga memotret perkembangan metode classifier yang digunakan beserta perkembangan penggunaan berbagai jenis sumber data citra yang menjadi masukan pada classifier. Berdasarkan hasil tabulasi dan analisa dari data-data yang dikumpulkan melalui studi ini diketahui bahwa jumlah penelitian terkait klasifikasi batubara dan gangue mengalami peningkatan dalam beberapa tahun terkahir yang disertai dengan penggunaan berbagai jenis metode classifier dan sumber data citra yang juga cukup bervariasi. Metode classifier yang cukup banyak digunakan adalah Convolutionary Neural Networks, dimana penggunaannya cukup dominan dengan angka lebih dari 60% diantara metode classifier lainnya. Sedangkan data citra optik menduduki peringkat atas sebagai sumber data citra yang paling banyak digunakan yaitu di level sekitar 60%. Di sisi lain, tren penggunaan data citra multispectral dan data citra thermal juga meningkat sebagai alternatif terhadap data citra optik yang cukup sensitif terhadap faktor lingkungan.
References
Alfarzaeai, M. S., Niu, Q., Zhao, J., Eshaq, R. M. A., & Hu, E. (2020). Coal/gangue recognition using convolutional neural networks and thermal images. IEEE Access, 8, 76780-76789. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9078124
Bai, F., Fan, M., Yang, H., & Dong, L. (2021). Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources. International Journal of Mining Science and Technology, 31(6), 1053-1061. https://www.sciencedirect.com/science/article/pii/S2095268621001014
Boon-itt, S. (2010). An empirical model of the relationship between manufacturing capabilities: Evidence from the Thai utomotive industry. NIDA Development Journal, 59(2), 19–45.
Cheng, G., Chen, J., Wei, Y., Chen, S., & Pan, Z. (2023). A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine. Symmetry, 15(1), 202. https://www.mdpi.com/2073-8994/15/1/202
Dou, D., Zhou, D., Yang, J., & Zhang, Y. (2018). Coal and gangue recognition under four operating conditions by using image analysis and Relief-SVM. International Journal of Coal Preparation and Utilization.
Dou, D., Wu, W., Yang, J., & Zhang, Y. (2019). Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM. Powder Technology, 356, 1024-1028.
Eshaq, R. M. A., Hu, E., Li, M., & Alfarzaeai, M. S. (2020). Separation between coal and gangue based on infrared radiation and visual extraction of the YCbCr color space. Ieee Access, 8, 55204-55220. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9039581
Eshaq, R. M. A., Hu, E., Qaid, H. A., Zhang, Y., & Liu, T. (2021). Using deep convolutional neural networks and infrared thermography to identify coal quality and gangue. IEEE Access, 9, 147315-147327. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9579439
Gao, K., Du, C., Wang, H., & Zhang, S. (2013). An efficient of coal and gangue recognition algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 6(4), 345-354. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c7d48c37cfd6705786ae8f284965099e8be967cb
Gao, R., Sun, Z., Li, W., Pei, L., Hu, Y., & Xiao, L. (2020). Automatic coal and gangue segmentation using u-net based fully convolutional networks. Energies, 13(4), 829. https://www.mdpi.com/1996-1073/13/4/829
Guo, Y., Wang, X., Wang, S., Hu, K., & Wang, W. (2021). Identification method of coal and coal gangue based on dielectric characteristics. Ieee Access, 9, 9845-9854. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9317851
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2011). A systematic literature review on fault prediction performance in software engineering. IEEE Transactions on Software Engineering, 38(6), 1276-1304. https://bura.brunel.ac.uk/bitstream/2438/5907/2/Fulltext.pdf
He, L., Wang, S., Guo, Y., Hu, K., Cheng, G., & Wang, X. (2023). Study of raw coal identification method by dual-energy X-ray and dual-view visible light imaging. International Journal of Coal Preparation and Utilization, 43(2), 361-376.
Hong, H., Zheng, L., Zhu, J., Pan, S., & Zhou, K. (2017). Automatic recognition of coal and gangue based on convolution neural network. arXiv preprint arXiv:1712.00720.
Hou, W. (2017). Identification of coal and gangue by feed-forward neural network based on data analysis. International Journal of Coal Preparation and Utilization, 39(1), 33-43.
Hu, F., Zhou, M., Yan, P., Bian, K., & Dai, R. (2019). Multispectral imaging: A new solution for identification of coal and gangue. IEEE Access, 7, 169697-169704. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8911443
Hu, F., Zhou, M., Dai, R., & Liu, Y. (2022). Recognition method of coal and gangue based on multispectral spectral characteristics combined with one-dimensional convolutional neural network. Frontiers in Earth Science, 10, 893485. https://www.frontiersin.org/articles/10.3389/feart.2022.893485/full
Hu, F., & Bian, K. (2022). Accurate identification strategy of coal and gangue using infrared imaging technology combined with convolutional neural network. IEEE Access, 10, 8758-8766. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9684859
Hu, F., Zhou, M., Yan, P., Liang, Z., & Li, M. (2022). A Bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging. Optics and Lasers in Engineering, 156, 107081.
Hu, F., Hu, Y., Cui, E., Guan, Y., Gao, B., Wang, X., & Yao, X. (2023). Recognition method of coal and gangue combined with structural similarity index measure and principal component analysis network under multispectral imaging. Microchemical Journal, 186, 108330.
Jiang, J., Han, Y., Zhao, H., Suo, J., & Cao, Q. (2021). Recognition and sorting of coal and gangue based on image process and multilayer perceptron. International Journal of Coal Preparation and Utilization, 43(1), 54-72.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report Version 2.3, EBSE-2007-.
Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering–a tertiary study. Information and software technology, 52(8), 792-805. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c3910095b25a674e7154acd9c38d0af220026e31
Lai, W., Zhou, M., Hu, F., Bian, K., & Song, H. (2020). A study of multispectral technology and two-dimension autoencoder for coal and gangue recognition. IEEE Access, 8, 61834-61843. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9049102
Li, D., Zhang, Z., Xu, Z., Xu, L., Meng, G., Li, Z., & Chen, S. (2019). An image-based hierarchical deep learning framework for coal and gangue detection. IEEE Access, 7, 184686-184699. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8936870
Li, L., Wang, H., & An, L. (2015). Research on recognition of coal and gangue based on image processing. World Journal of Engineering, 12(3), 247-254.
Li, M., Duan, Y., He, X., & Yang, M. (2020). Image positioning and identification method and system for coal and gangue sorting robot. International Journal of Coal Preparation and Utilization, 42(6), 1759-1777.
Li, M., & Sun, K. (2018, August). An image recognition approach for coal and gangue used in pick-up robot. In 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) (pp. 501-507). Ieee.
Li, N., & Gong, X. (2021). An image preprocessing model of coal and gangue in high dust and low light conditions based on the joint enhancement algorithm. Computational Intelligence and Neuroscience, 2021.
Liu, K., Zhang, X., & Chen, Y. (2018). Extraction of coal and gangue geometric features with multifractal detrending fluctuation analysis. Applied Sciences, 8(3), 463. https://www.mdpi.com/2076-3417/8/3/463
Liu, H., & Xu, K. (2023). Recognition of gangues from color images using convolutional neural networks with attention mechanism. Measurement, 206, 112273.
Liu, X., Jing, W., Zhou, M., & Li, Y. (2019). Multi-scale feature fusion for coal-rock recognition based on completed local binary pattern and convolution neural network. Entropy, 21(6), 622. https://www.mdpi.com/1099-4300/21/6/622
Pan, H., Shi, Y., Lei, X., Wang, Z., & Xin, F. (2022). Fast identification model for coal and gangue based on the improved tiny YOLO v3. Journal of Real-Time Image Processing, 19(3), 687-701.
Pu, Y., Apel, D. B., Szmigiel, A., & Chen, J. (2019). Image recognition of coal and coal gangue using a convolutional neural network and transfer learning. Energies, 12(9), 1735. https://www.mdpi.com/1996-1073/12/9/1735
Sun, J., & Su, B. (2013). Coal–rock interface detection on the basis of image texture features. International Journal of Mining Science and Technology, 23(5), 681-687.
Sun, Z., Huang, L., & Jia, R. (2021). Coal and gangue separating robot system based on computer vision. Sensors, 21(4), 1349. https://www.mdpi.com/1424-8220/21/4/1349
Sun, Z., Lu, W., Xuan, P., Li, H., Zhang, S., Niu, S., & Jia, R. (2022). Separation of gangue from coal based on supplementary texture by morphology. International Journal of Coal Preparation and Utilization, 42(3), 221-237.
Wahono, R. S. (2015). A systematic literature review of software defect prediction. Journal of software engineering, 1(1), 1-16.
Wan, L., Wang, J., Zeng, Q., Ma, D., Yu, X., & Meng, Z. (2022). Vibration response analysis of the tail beam of hydraulic support impacted by coal gangue particles with different shapes. ACS omega, 7(4), 3656-3670.
Wang, D., Ni, J., & Du, T. (2022). An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network. Symmetry, 14(5), 880. https://www.mdpi.com/2073-8994/14/5/880
Wang, W., & Zhang, C. (2017). Separating coal and gangue using three-dimensional laser scanning. International Journal of Mineral Processing, 169, 79-84.
Wang, W., Lv, Z., & Lu, H. (2018). Research on methods to differentiate coal and gangue using image processing and a support vector machine. International Journal of Coal Preparation and Utilization.
Wang, X., Wang, S., Guo, Y., Hu, K., & Wang, W. (2021). Dielectric and geometric feature extraction and recognition method of coal and gangue based on VMD-SVM. Powder Technology, 392, 241-250.
Wei, D., Li, J., Li, B., Wang, X., Chen, S., Wang, X., & Wang, L. (2023). A fast recognition method for coal gangue image processing. Multimedia Systems, 1-13.
Xie, Y., Chi, X., Li, H., Wang, F., Yan, L., Zhang, B., & Zhang, Q. (2021). Coal and gangue recognition method based on local texture classification network for robot picking. Applied Sciences, 11(23), 11495. https://www.mdpi.com/2076-3417/11/23/11495
Xue, H., Hu, B. H., Zhao, X. Y., Liu, E. M., & Ding, W. J. (2014). Study on characteristic extraction of coal and rock at mechanized top coal caving face based on image gray scale. In Applied Mechanics and Materials (Vol. 678, pp. 193-196). Trans Tech Publications Ltd.
Xue, G., Hou, P., Li, S., Qian, X., Han, S., & Gao, S. (2023). Coal Gangue Recognition during Coal Preparation Using an Adaptive Boosting Algorithm. Minerals, 13(3), 329. https://www.mdpi.com/2075-163X/13/3/329
Xue, G., Li, S., Hou, P., Gao, S., & Tan, R. (2023). Research on lightweight Yolo coal gangue detection algorithm based on resnet18 backbone feature network. Internet of Things, 22, 100762. https://www.sciencedirect.com/science/article/pii/S2542660523000859
Yan, P., Kan, X., Zhang, H., Zhang, X., Chen, F., & Li, X. (2023). Target Recognition of Coal and Gangue Based on Improved YOLOv5s and Spectral Technology. Sensors, 23(10), 4911. https://www.mdpi.com/1424-8220/23/10/4911
Yang, D., Li, J., Zheng, K., Du, C., & Liu, S. (2018). Impact-crush separation characteristics of coal and gangue. International Journal of Coal Preparation and Utilization, 38(3), 127-134.
Yang, J., Chang, B., Zhang, Y., Luo, W., & Wu, M. (2021). Research on CNN Coal and Rock Recognition Method Based on Hyperspectral Data. https://www.researchsquare.com/article/rs-501935/v1
Yang, J., Chang, B., Zhang, Y., Luo, W., Ge, S., & Wu, M. (2022). CNN coal and rock recognition method based on hyperspectral data. International Journal of Coal Science & Technology, 9(1), 63. https://link.springer.com/article/10.1007/s40789-022-00516-x
Zhang, N., & Liu, C. (2018). Radiation characteristics of natural gamma-ray from coal and gangue for recognition in top https://www.nature.com/articles/s41598-017-18625-y coal caving. Scientific Reports, 8(1), 190. https://www.nature.com/articles/s41598-017-18625-y
Zhang, L., Sui, Y., Wang, H., Hao, S., & Zhang, N. (2022). Image feature extraction and recognition model construction of coal and gangue based on image processing technology. Scientific Reports, 12(1), 20983. https://www.nature.com/articles/s41598-022-25496-5
Zhang, Q., Gu, J., & Liu, J. (2021). Research on coal and rock type recognition based on mechanical vision. Shock and Vibration, 2021, 1-10. https://www.hindawi.com/journals/sv/2021/6617717/
Zhang, Y., Zhu, H., Zhu, J., Ou, Z., Shen, T., Sun, J., & Feng, A. (2021). Experimental study on separation of lumpish coal and gangue using X-ray. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-13. https://www.tandfonline.com/doi/abs/10.1080/15567036.2021.1976325
Zhang, Y., Wang, J., Yu, Z., Zhao, S., & Bei, G. (2022). Research on intelligent detection of coal gangue based on deep learning. Measurement, 198, 111415. https://www.sciencedirect.com/science/article/abs/pii/S0263224122006479
Zhao, Y. D., & He, X. (2013). Recognition of coal and gangue based on X-ray. In Applied mechanics and materials (Vol. 275, pp. 2350-2353). Trans Tech Publications Ltd.
Zhao, Y., Wang, S., Guo, Y., Cheng, G., He, L., & Wang, W. (2022). The identification of coal and gangue and the prediction of the degree of coal metamorphism based on the EDXRD principle and the PSO-SVM model. Gospodarka Surowcami Mineralnymi, 38(2).
Zhou, M., & Lai, W. (2023). Coal gangue recognition based on spectral imaging combined with XGBoost. PloS one, 18(1), e0279955. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279955
Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y. (2017). Classification of surface defects on steel sheet using convolutional neural networks. Mater. Technol, 51(1), 123-131. http://mit.imt.si/izvodi/mit171/zhou.pdf
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