Detecting coal content in gangue via machine vision and genetic algorithm-backpropagation neural network
•Three-dimensional (3D) features were extracted to predict coal content in gangue.•Eight optimal features were selected according to Spearman correlation coefficient.•Original model, 2D model, Non-optimized model and 3D model were built and compared. Coal content in gangue is one of the important te...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 201; S. 111739 |
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| Sprache: | Englisch |
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30.09.2022
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| Abstract | •Three-dimensional (3D) features were extracted to predict coal content in gangue.•Eight optimal features were selected according to Spearman correlation coefficient.•Original model, 2D model, Non-optimized model and 3D model were built and compared.
Coal content in gangue is one of the important technical and economic indicators of coal preparation plants and cannot be detected online at present. A novel approach based on binocular machine vision and genetic algorithm-backpropagation neural network (GA-BPNN) was proposed. First, the sample image was segmented, and each region was judged to be coal or gangue. Four size and eleven density features were extracted from each region. For the size features, the values of each feature were summed up by class, whereas the average operation was performed for the density features. The values of gangue features were divided by those of coal features to obtain an initial feature set based on a two-dimensional (2D) image. By contrast, binocular images were collected to estimate a three-dimensional (3D) feature, that is, the height features of coal and gangue particles. Subsequently, the projected area and the height feature were multiplied to obtain another 3D feature, that is, the volume feature. The Spearman correlation coefficient was adopted as the feature selection method, and eight optimal features were extracted from the final feature set, including both 2D and 3D features. Then, the coal content in gangue was modeled using a BPNN optimized by GA. Finally, three models were built based on 17 complete features, 6 optimal 2D features, and 8 optimal 3D features; another model without GA optimization was also built. The gangue sample experiment of Hongliu Coal Preparation Plant showed that the average relative error of our method was 7.98%, which is much better than that of the other three models. |
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| AbstractList | •Three-dimensional (3D) features were extracted to predict coal content in gangue.•Eight optimal features were selected according to Spearman correlation coefficient.•Original model, 2D model, Non-optimized model and 3D model were built and compared.
Coal content in gangue is one of the important technical and economic indicators of coal preparation plants and cannot be detected online at present. A novel approach based on binocular machine vision and genetic algorithm-backpropagation neural network (GA-BPNN) was proposed. First, the sample image was segmented, and each region was judged to be coal or gangue. Four size and eleven density features were extracted from each region. For the size features, the values of each feature were summed up by class, whereas the average operation was performed for the density features. The values of gangue features were divided by those of coal features to obtain an initial feature set based on a two-dimensional (2D) image. By contrast, binocular images were collected to estimate a three-dimensional (3D) feature, that is, the height features of coal and gangue particles. Subsequently, the projected area and the height feature were multiplied to obtain another 3D feature, that is, the volume feature. The Spearman correlation coefficient was adopted as the feature selection method, and eight optimal features were extracted from the final feature set, including both 2D and 3D features. Then, the coal content in gangue was modeled using a BPNN optimized by GA. Finally, three models were built based on 17 complete features, 6 optimal 2D features, and 8 optimal 3D features; another model without GA optimization was also built. The gangue sample experiment of Hongliu Coal Preparation Plant showed that the average relative error of our method was 7.98%, which is much better than that of the other three models. |
| ArticleNumber | 111739 |
| Author | Huang, Zixuan Zhang, Chunlong Dou, Dongyang Sun, Fengjie |
| Author_xml | – sequence: 1 givenname: Chunlong surname: Zhang fullname: Zhang, Chunlong organization: Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, PR China – sequence: 2 givenname: Dongyang surname: Dou fullname: Dou, Dongyang email: ddy41@cumt.edu.cn organization: Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, PR China – sequence: 3 givenname: Fengjie surname: Sun fullname: Sun, Fengjie email: baiyu5415@sina.com organization: Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, PR China – sequence: 4 givenname: Zixuan surname: Huang fullname: Huang, Zixuan organization: Key Laboratory of Coal Processing and Efficient Utilization of Ministry of Education, China University of Mining and Technology, Xuzhou 221116, PR China |
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| Cites_doi | 10.1002/cjce.23860 10.1016/j.istruc.2021.12.055 10.1038/323533a0 10.1109/TASE.2018.2840345 10.1049/iet-ipr.2019.1055 10.1080/19392699.2022.2051011 10.1080/19392699.2021.1932842 10.1088/1742-6596/1574/1/012173 10.1016/j.optlaseng.2019.06.011 10.1504/IJOGCT.2016.074768 10.1016/j.measurement.2020.108663 10.1016/j.measurement.2018.08.026 10.1109/RCAR.2018.8621725 10.1016/j.rcim.2019.03.001 10.1109/ACCESS.2020.2981534 10.1016/j.powtec.2006.03.017 10.1016/j.powtec.2022.117669 10.1109/ICICTA.2009.142 10.1080/15567036.2013.872718 10.1007/s11042-018-7092-0 10.1109/CVPR.2005.56 10.3788/LOP202158.0215004 10.1016/j.minpro.2011.07.008 10.1109/ICINFA.2010.5512011 |
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| References | Yu Jiexiao, Zhang Meiqi, Su Yuting, Three-dimensional vehicle detection algorithm based on binocular vision, Laser Optoelectronics PROGRESS(2), 2021. doi: https://doi.org/10.3788/L0P202158.0215004. Robert Singh, Chaudhury (b0035) 2020; 14 Tang, Zhu, Chen, Wu, Chen, Li, Li (b0060) 2022; 37 Yongzhou Wan, et al., Prediction of BP neural network and preliminary application for suppression of low‐temperature oxidation of coal stockpiles by pulverized coal covering, Canad. J. Chem. Eng. 98.12 (2020). doi: https://doi.org/10.1002/cjce.23860. Zhang, Yang (b0090) 2015; 37 X.-M. Ma, Coal Gangue Image Identification and Classification with Wavelet Transform, 2009, in: doi: https://doi.org/10.1109/icicta.2009.142. H. Hirschmuller, n.d. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information, IEEE Conference on Computer Vision and Pattern Recognition. doi: https://doi.org/10.1109/cvpr.2005.56. Wenfei, Yingjie, Ling (b0135) 2009; 09 Tang, Li, Wang, Chen, Feng, Zou, Huang (b0065) 2019; 59 Qi, Dou (b0140) 2022; 407 Zhu, Wu, Wang, Zhang (b0040) 2020; 79 Wang, Huang, Dou, Qiu (b0050) 2022; 42 Chen, Tang, Zou, Huang, Li, He (b0070) 2019; 122 Tang, Li, Feng, Liu, Zou, Chen (b0075) 2018; 130 M. Li, K. Sun, An Image Recognition Approach for Coal and Gangue Used in Pick-Up Robot, 2018. in: .. doi: https://doi.org/10.1109/rcar.2018.8621725. X. Zhang P. Shen J. Gao X. X, D. Qi L. Zhang A. Xue X. Liang X. Chen, A license plate recognition system based on Tamura texture in complex conditions, International Conference on Information and Automation (ICIA), 2010, doi: https://doi.org/10.1109/icinfa.2010.5512011. Perez, Estevez, Vera, Castillo, Aravena, Schulz, Medina (b0105) 2011; 101 Zhang, Yang (b0100) 2016; 11 Mi, Zheng (b0080) 2020; 5 Wang (b0095) 2006; 165 Li, Su, Yang, Zhang (b0115) 2014; 2014 Lv, Jiao (b0125) 2020; 1574 Qiu, Dou, Zhou, Yang (b0005) 2021; 173 Wang, Li, Chen, Diekel, Jia (b0025) 2019; 16 Haoxiang Huang, Dongyang Dou, Gangyang Liu, Modeling of coal and gangue volume based onshape clustering and image analysis, Int. J. Coal Preparation Utilizat. doi: https://doi.org/10.1080/19392699.2022.2051011. Rumelhart, Hinton, Williams (b0130) 1986; 323 Eshaq, Hu, Li, Alfarzaeai (b0010) 2020; 8 Yang Chunyu, Zhen, Linna (b0055) 2021; 08 Wang (10.1016/j.measurement.2022.111739_b0095) 2006; 165 10.1016/j.measurement.2022.111739_b0015 Rumelhart (10.1016/j.measurement.2022.111739_b0130) 1986; 323 Qiu (10.1016/j.measurement.2022.111739_b0005) 2021; 173 Mi (10.1016/j.measurement.2022.111739_b0080) 2020; 5 Chen (10.1016/j.measurement.2022.111739_b0070) 2019; 122 Wang (10.1016/j.measurement.2022.111739_b0025) 2019; 16 10.1016/j.measurement.2022.111739_b0110 Li (10.1016/j.measurement.2022.111739_b0115) 2014; 2014 10.1016/j.measurement.2022.111739_b0030 Zhu (10.1016/j.measurement.2022.111739_b0040) 2020; 79 Wang (10.1016/j.measurement.2022.111739_b0050) 2022; 42 Tang (10.1016/j.measurement.2022.111739_b0065) 2019; 59 Robert Singh (10.1016/j.measurement.2022.111739_b0035) 2020; 14 Lv (10.1016/j.measurement.2022.111739_b0125) 2020; 1574 Qi (10.1016/j.measurement.2022.111739_b0140) 2022; 407 Tang (10.1016/j.measurement.2022.111739_b0060) 2022; 37 Tang (10.1016/j.measurement.2022.111739_b0075) 2018; 130 Zhang (10.1016/j.measurement.2022.111739_b0090) 2015; 37 Eshaq (10.1016/j.measurement.2022.111739_b0010) 2020; 8 Zhang (10.1016/j.measurement.2022.111739_b0100) 2016; 11 Perez (10.1016/j.measurement.2022.111739_b0105) 2011; 101 Yang Chunyu (10.1016/j.measurement.2022.111739_b0055) 2021; 08 10.1016/j.measurement.2022.111739_b0120 10.1016/j.measurement.2022.111739_b0045 Wenfei (10.1016/j.measurement.2022.111739_b0135) 2009; 09 10.1016/j.measurement.2022.111739_b0020 10.1016/j.measurement.2022.111739_b0085 |
| References_xml | – reference: M. Li, K. Sun, An Image Recognition Approach for Coal and Gangue Used in Pick-Up Robot, 2018. in: .. doi: https://doi.org/10.1109/rcar.2018.8621725. – volume: 37 start-page: 426 year: 2022 end-page: 441 ident: b0060 article-title: Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method publication-title: Structures – reference: Yongzhou Wan, et al., Prediction of BP neural network and preliminary application for suppression of low‐temperature oxidation of coal stockpiles by pulverized coal covering, Canad. J. Chem. Eng. 98.12 (2020). doi: https://doi.org/10.1002/cjce.23860. – volume: 2014 start-page: 1 year: 2014 end-page: 8 ident: b0115 article-title: Target Image Matching Algorithm Based on Binocular CCD Ranging publication-title: Abstract Appl. Anal. – volume: 08 start-page: 164 year: 2021 end-page: 174 ident: b0055 article-title: Binocular vision measurement of coal flow on belt conveyor based on deep learning publication-title: Chin. J. Scientific Instrum. – volume: 5 start-page: 219 year: 2020 ident: b0080 article-title: Binocular vision vehicle environment collision early warning method based on machine learning publication-title: Int. J. Vehicle Informat. Commun. Syst. – volume: 165 start-page: 1 year: 2006 end-page: 10 ident: b0095 article-title: Image analysis of particles by modified Ferret method—best-fit rectangle publication-title: Powder Technol. – reference: H. Hirschmuller, n.d. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information, IEEE Conference on Computer Vision and Pattern Recognition. doi: https://doi.org/10.1109/cvpr.2005.56. – reference: X.-M. Ma, Coal Gangue Image Identification and Classification with Wavelet Transform, 2009, in: doi: https://doi.org/10.1109/icicta.2009.142. – volume: 407 start-page: 117669 year: 2022 ident: b0140 article-title: Prediction of density and sulfur content level of high-sulfur coal based on image processing publication-title: Powder Technol. – volume: 09 start-page: 54 year: 2009 end-page: 56 ident: b0135 article-title: The basic principles of genetic algorithm and its application research publication-title: Software Guide – volume: 37 start-page: 181 year: 2015 end-page: 191 ident: b0090 article-title: The density fraction estimation of coarse coal by use of the kernel method and machine vision publication-title: Energy Sources Part A – volume: 1574 start-page: 012173 year: 2020 ident: b0125 article-title: Experiment of Stereo matching algorithm based on binocular vision publication-title: J. Phys. Conf. Ser. – volume: 42 start-page: 1915 year: 2022 end-page: 1924 ident: b0050 article-title: Detection of coal content in gangue via image analysis and particle swarm optimization–support vector machine publication-title: Int. J. Coal Preparat. Utilizat. – reference: Yu Jiexiao, Zhang Meiqi, Su Yuting, Three-dimensional vehicle detection algorithm based on binocular vision, Laser Optoelectronics PROGRESS(2), 2021. doi: https://doi.org/10.3788/L0P202158.0215004. – volume: 8 start-page: 55204 year: 2020 end-page: 55220 ident: b0010 article-title: Separation between coal and gangue based on infrared radiation and visual extraction of the YCbCr color space publication-title: IEEE Access – volume: 59 start-page: 36 year: 2019 end-page: 46 ident: b0065 article-title: Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision publication-title: Robotics Comput. Integr. Manuf. – reference: X. Zhang P. Shen J. Gao X. X, D. Qi L. Zhang A. Xue X. Liang X. Chen, A license plate recognition system based on Tamura texture in complex conditions, International Conference on Information and Automation (ICIA), 2010, doi: https://doi.org/10.1109/icinfa.2010.5512011. – volume: 130 start-page: 372 year: 2018 end-page: 383 ident: b0075 article-title: Binocular vision measurement and its application in full-field convex deformation of concrete-filled steel tubular columns publication-title: Measurement – volume: 79 start-page: 14539 year: 2020 end-page: 14551 ident: b0040 article-title: Identification of grape diseases using image analysis and BP neural networks publication-title: Multimedia Tools Appl. – volume: 122 start-page: 170 year: 2019 end-page: 183 ident: b0070 article-title: High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm publication-title: Opt. Lasers Eng. – volume: 14 start-page: 2532 year: 2020 end-page: 2540 ident: b0035 article-title: Comparative analysis of texture feature extraction techniques for rice grain classification publication-title: IET Image Proc. – volume: 101 start-page: 28 year: 2011 end-page: 36 ident: b0105 article-title: Ore grade estimation by feature selection and voting using boundary detection in digital image analysis publication-title: Int. J. Miner. Process. – volume: 323 start-page: 533 year: 1986 end-page: 536 ident: b0130 article-title: Learning representations by back-propagating errors publication-title: Nature – volume: 11 start-page: 279 year: 2016 end-page: 289 ident: b0100 article-title: Narrow density fraction prediction of coarse coal by image analysis and MIV-SVM publication-title: Int. J. Oil Gas Coal Technol. – volume: 173 start-page: 108663 year: 2021 ident: b0005 article-title: On-line prediction of clean coal ash content based on image analysis publication-title: Measurement – volume: 16 start-page: 640 year: 2019 end-page: 653 ident: b0025 article-title: Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations publication-title: IEEE Trans. Autom. Sci. Eng. – reference: Haoxiang Huang, Dongyang Dou, Gangyang Liu, Modeling of coal and gangue volume based onshape clustering and image analysis, Int. J. Coal Preparation Utilizat. doi: https://doi.org/10.1080/19392699.2022.2051011. – ident: 10.1016/j.measurement.2022.111739_b0045 doi: 10.1002/cjce.23860 – volume: 37 start-page: 426 year: 2022 ident: 10.1016/j.measurement.2022.111739_b0060 article-title: Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method publication-title: Structures doi: 10.1016/j.istruc.2021.12.055 – volume: 323 start-page: 533 issue: 6088 year: 1986 ident: 10.1016/j.measurement.2022.111739_b0130 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – volume: 16 start-page: 640 issue: 2 year: 2019 ident: 10.1016/j.measurement.2022.111739_b0025 article-title: Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2018.2840345 – volume: 14 start-page: 2532 issue: 11 year: 2020 ident: 10.1016/j.measurement.2022.111739_b0035 article-title: Comparative analysis of texture feature extraction techniques for rice grain classification publication-title: IET Image Proc. doi: 10.1049/iet-ipr.2019.1055 – ident: 10.1016/j.measurement.2022.111739_b0030 doi: 10.1080/19392699.2022.2051011 – volume: 42 start-page: 1915 issue: 7 year: 2022 ident: 10.1016/j.measurement.2022.111739_b0050 article-title: Detection of coal content in gangue via image analysis and particle swarm optimization–support vector machine publication-title: Int. J. Coal Preparat. Utilizat. doi: 10.1080/19392699.2021.1932842 – volume: 1574 start-page: 012173 issue: 1 year: 2020 ident: 10.1016/j.measurement.2022.111739_b0125 article-title: Experiment of Stereo matching algorithm based on binocular vision publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1574/1/012173 – volume: 2014 start-page: 1 year: 2014 ident: 10.1016/j.measurement.2022.111739_b0115 article-title: Target Image Matching Algorithm Based on Binocular CCD Ranging publication-title: Abstract Appl. Anal. – volume: 122 start-page: 170 year: 2019 ident: 10.1016/j.measurement.2022.111739_b0070 article-title: High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm publication-title: Opt. Lasers Eng. doi: 10.1016/j.optlaseng.2019.06.011 – volume: 09 start-page: 54 year: 2009 ident: 10.1016/j.measurement.2022.111739_b0135 article-title: The basic principles of genetic algorithm and its application research publication-title: Software Guide – volume: 11 start-page: 279 issue: 3 year: 2016 ident: 10.1016/j.measurement.2022.111739_b0100 article-title: Narrow density fraction prediction of coarse coal by image analysis and MIV-SVM publication-title: Int. J. Oil Gas Coal Technol. doi: 10.1504/IJOGCT.2016.074768 – volume: 5 start-page: 219 issue: 2 year: 2020 ident: 10.1016/j.measurement.2022.111739_b0080 article-title: Binocular vision vehicle environment collision early warning method based on machine learning publication-title: Int. J. Vehicle Informat. Commun. Syst. – volume: 173 start-page: 108663 year: 2021 ident: 10.1016/j.measurement.2022.111739_b0005 article-title: On-line prediction of clean coal ash content based on image analysis publication-title: Measurement doi: 10.1016/j.measurement.2020.108663 – volume: 130 start-page: 372 year: 2018 ident: 10.1016/j.measurement.2022.111739_b0075 article-title: Binocular vision measurement and its application in full-field convex deformation of concrete-filled steel tubular columns publication-title: Measurement doi: 10.1016/j.measurement.2018.08.026 – ident: 10.1016/j.measurement.2022.111739_b0015 doi: 10.1109/RCAR.2018.8621725 – volume: 59 start-page: 36 year: 2019 ident: 10.1016/j.measurement.2022.111739_b0065 article-title: Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision publication-title: Robotics Comput. Integr. Manuf. doi: 10.1016/j.rcim.2019.03.001 – volume: 8 start-page: 55204 year: 2020 ident: 10.1016/j.measurement.2022.111739_b0010 article-title: Separation between coal and gangue based on infrared radiation and visual extraction of the YCbCr color space publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2981534 – volume: 165 start-page: 1 issue: 1 year: 2006 ident: 10.1016/j.measurement.2022.111739_b0095 article-title: Image analysis of particles by modified Ferret method—best-fit rectangle publication-title: Powder Technol. doi: 10.1016/j.powtec.2006.03.017 – volume: 08 start-page: 164 year: 2021 ident: 10.1016/j.measurement.2022.111739_b0055 article-title: Binocular vision measurement of coal flow on belt conveyor based on deep learning publication-title: Chin. J. Scientific Instrum. – volume: 407 start-page: 117669 year: 2022 ident: 10.1016/j.measurement.2022.111739_b0140 article-title: Prediction of density and sulfur content level of high-sulfur coal based on image processing publication-title: Powder Technol. doi: 10.1016/j.powtec.2022.117669 – ident: 10.1016/j.measurement.2022.111739_b0020 doi: 10.1109/ICICTA.2009.142 – volume: 37 start-page: 181 issue: 2 year: 2015 ident: 10.1016/j.measurement.2022.111739_b0090 article-title: The density fraction estimation of coarse coal by use of the kernel method and machine vision publication-title: Energy Sources Part A doi: 10.1080/15567036.2013.872718 – volume: 79 start-page: 14539 issue: 21-22 year: 2020 ident: 10.1016/j.measurement.2022.111739_b0040 article-title: Identification of grape diseases using image analysis and BP neural networks publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-018-7092-0 – ident: 10.1016/j.measurement.2022.111739_b0120 doi: 10.1109/CVPR.2005.56 – ident: 10.1016/j.measurement.2022.111739_b0085 doi: 10.3788/LOP202158.0215004 – volume: 101 start-page: 28 issue: 1–4 year: 2011 ident: 10.1016/j.measurement.2022.111739_b0105 article-title: Ore grade estimation by feature selection and voting using boundary detection in digital image analysis publication-title: Int. J. Miner. Process. doi: 10.1016/j.minpro.2011.07.008 – ident: 10.1016/j.measurement.2022.111739_b0110 doi: 10.1109/ICINFA.2010.5512011 |
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| Snippet | •Three-dimensional (3D) features were extracted to predict coal content in gangue.•Eight optimal features were selected according to Spearman correlation... |
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| SubjectTerms | Binocular Stereo Vision BP neural network Coal content in gangue Machine vision |
| Title | Detecting coal content in gangue via machine vision and genetic algorithm-backpropagation neural network |
| URI | https://dx.doi.org/10.1016/j.measurement.2022.111739 |
| Volume | 201 |
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