{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T02:47:30Z","timestamp":1777085250484,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Provincial University System Innovation Project of China","award":["GXXT-2021-076"],"award-info":[{"award-number":["GXXT-2021-076"]}]},{"name":"Anhui Provincial University System Innovation Project of China","award":["EC2021010"],"award-info":[{"award-number":["EC2021010"]}]},{"name":"Anhui Provincial University System Innovation Project of China","award":["JKF22-06"],"award-info":[{"award-number":["JKF22-06"]}]},{"name":"Open Research Fund of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining","award":["GXXT-2021-076"],"award-info":[{"award-number":["GXXT-2021-076"]}]},{"name":"Open Research Fund of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining","award":["EC2021010"],"award-info":[{"award-number":["EC2021010"]}]},{"name":"Open Research Fund of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining","award":["JKF22-06"],"award-info":[{"award-number":["JKF22-06"]}]},{"name":"Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering &amp; Resources Recycling","award":["GXXT-2021-076"],"award-info":[{"award-number":["GXXT-2021-076"]}]},{"name":"Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering &amp; Resources Recycling","award":["EC2021010"],"award-info":[{"award-number":["EC2021010"]}]},{"name":"Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering &amp; Resources Recycling","award":["JKF22-06"],"award-info":[{"award-number":["JKF22-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.<\/jats:p>","DOI":"10.3390\/sym15010202","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T02:27:12Z","timestamp":1673317632000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine"],"prefix":"10.3390","volume":"15","author":[{"given":"Gang","family":"Cheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China"},{"name":"School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China"},{"name":"School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sensen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeye","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","first-page":"2651","article-title":"Sustainable coal mining and mining sciences","volume":"41","author":"Wang","year":"2016","journal-title":"J. China Coal Soc."},{"key":"ref_2","first-page":"11","article-title":"Present situation and Prospect of coal gangue treatment technology","volume":"37","author":"Zhou","year":"2020","journal-title":"J. Min. Saf. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.fuel.2019.05.118","article-title":"Extraction of SiO2 and Al2O3 from coal gangue actuvated by supercritical water","volume":"253","author":"Han","year":"2019","journal-title":"Fuel"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.hydromet.2015.04.018","article-title":"Separation of aluminum and silica from coal gangue by elevated temperature acid leaching for the preparation of alumina and SiC","volume":"155","author":"Xiao","year":"2015","journal-title":"Hydrometallurgy"},{"key":"ref_5","first-page":"295","article-title":"2025 scenarios and development path of intelligent coal mine","volume":"43","author":"Wang","year":"2018","journal-title":"J. China Coal Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5233845","DOI":"10.1155\/2022\/5233845","article-title":"Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model","volume":"2022","author":"Bai","year":"2022","journal-title":"Comput. Intell. Neurosc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1080\/00206814.2017.1378131","article-title":"Stone coal in China: A review","volume":"60","author":"Dai","year":"2018","journal-title":"Int. Geol. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1016\/j.resconrec.2010.05.005","article-title":"Recycling utilization patterns of coal mining waste in China","volume":"54","author":"Liu","year":"2010","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.18280\/ts.360204","article-title":"Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection","volume":"36","author":"Zou","year":"2019","journal-title":"Trait. du Signal"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106784","DOI":"10.1109\/ACCESS.2019.2932118","article-title":"Vibration Test of Single Coal Gangue Particle Directly Impacting the Metal Plate and the Study of Coal Gangue Recognition Based on Vibration Signal and Stacking Integration","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/19392699.2016.1207634","article-title":"Impact-Crush Separation Characteristics of Coal and Gangue","volume":"38","author":"Yang","year":"2018","journal-title":"Int. J. Coal Prep. Util."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6566","DOI":"10.1364\/AO.422498","article-title":"Coal and gangue identification method based on the intensity image of lidar and DenseNet","volume":"60","author":"Xing","year":"2021","journal-title":"Appl. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.minpro.2017.10.010","article-title":"Separating coal and gangue using three-dimensional laser scanning","volume":"169","author":"Wang","year":"2017","journal-title":"Int. J. Miner. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"169697","DOI":"10.1109\/ACCESS.2019.2955725","article-title":"Multispectral Imaging: A New Solution for Identification of Coal and Gangue","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108330","DOI":"10.1016\/j.microc.2022.108330","article-title":"Recognition method of coal and gangue combined with structural similarity index measure and principal component analysis network under multispectral imaging","volume":"186","author":"Hu","year":"2023","journal-title":"Microchem. J."},{"key":"ref_16","first-page":"169697","article-title":"Coal\/Gangue Recognition Using Convolutional Neural Networks and Thermal Images","volume":"7","author":"Alfarzaeai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"8043","article-title":"Nondestructive Identification of Coal and Gangue via Near-Infrared Spectroscopy Based on Improved Broad Learning","volume":"69","author":"Zou","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","first-page":"416","article-title":"Research on coal and gangue classification method based on combined analysis of visible-near infrared and thermal infrared spectroscopy","volume":"37","author":"Song","year":"2017","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Song, L., Yu, Y., Yan, Z., Xiao, D., Sun, Y., Zhang, X., Li, X., Cheng, B., Gao, H., and Bai, D. (2022). Rapid Analysis of Composition of Coal Gangue Based on Deep Learning and Thermal Infrared Spectroscopy. Sustainability, 14.","DOI":"10.3390\/su142316210"},{"key":"ref_20","first-page":"85","article-title":"Design of separation system of coal and gangue based on X-ray and machine vision","volume":"43","author":"Yang","year":"2017","journal-title":"Ind. Mine Autom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1038\/s41598-017-18625-y","article-title":"Radiation characteristics of natural gamma-ray from coal and gangue for recognition in top coal caving","volume":"8","author":"Zhang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fan, C.-L., and Chung, Y.-J. (2022). Supervised Machine Learning-Based Detection of Concrete Efflorescence. Symmetry, 14.","DOI":"10.3390\/sym14112384"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41563-019-0339-y","article-title":"Leveraging machine vision in cell-based diagnostics to do more with less","volume":"18","author":"Doan","year":"2019","journal-title":"Nat. Mater."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, H., Dou, D., and Zhang, C. (2022). Detecting coal-carrying rate in gangue based on binocular machine vision and particle queuing method. Int. J. Coal Prep. Util., 1\u201313.","DOI":"10.1080\/19392699.2022.2104265"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.powtec.2018.06.035","article-title":"Automatic characterisation of chars from the combusion of pulverised coals using machine vision","volume":"338","author":"Chaves","year":"2018","journal-title":"Powder Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"041217","DOI":"10.1117\/1.JEI.31.4.041217","article-title":"Feature extraction method CNDFA for target contour of coal and gangue based on multifractal","volume":"31","author":"Li","year":"2022","journal-title":"J. Electron. Imaging"},{"key":"ref_27","first-page":"57","article-title":"Coal-gangue image recognition in fully-mechanized caving face based on random forest","volume":"46","author":"Xue","year":"2020","journal-title":"Ind. Mine Autom."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, X., Zhang, P., Meng, F., and Liu, C. (2022). A Coal Seam Thickness Prediction Model Based on CPSAC and WOA-LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield. Energies, 15.","DOI":"10.3390\/en15197324"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"77599","DOI":"10.1109\/ACCESS.2021.3081442","article-title":"Recognition Methods for Coal and Coal Gangue Based on Deep Learning","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gao, R., Sun, Z., Li, W., Pei, L., Hu, Y., and Xiao, L. (2020). Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks. Energies, 13.","DOI":"10.3390\/en13040829"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1049\/ipr2.12339","article-title":"Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3","volume":"16","author":"Li","year":"2022","journal-title":"Int. Image Process"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, D., Ni, J., and Du, T. (2022). An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network. Symmetry, 14.","DOI":"10.3390\/sym14050880"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.patcog.2017.03.011","article-title":"Support vector machine classifier with truncated pinball loss","volume":"68","author":"Shen","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, C., Peng, T., and Zhu, Y. (2021). A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM. Appl. Sci., 11.","DOI":"10.3390\/app11199081"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1007\/s11227-018-2554-8","article-title":"Facial expression recognition using iterative fusion of MO-HOG and deep features","volume":"76","author":"Wang","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6995","DOI":"10.1007\/s11042-022-12020-0","article-title":"An optimal method based on HOG-SVM for fault detection","volume":"81","author":"Xu","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neucom.2012.10.032","article-title":"A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image","volume":"120","author":"Yang","year":"2013","journal-title":"Neurocompuying"},{"key":"ref_38","unstructured":"Ojala, T., Pietikainen, M., and Harwood, D. (1994, January 9\u201313). Performance evalution of texture measure with classification based on Kullback discrimination of distribution. Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machine for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, Y., Li, T., Xie, X., and Chang, C. (2020). The Short-Term Forecasting of Asymmetry Photovoltaic Power Based on the Feature Extraction of PV Power and SVM Algorithm. Symmetry, 12.","DOI":"10.3390\/sym12111777"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: Past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1080\/19392699.2021.1932842","article-title":"Detection of coal content in gangue via image analysis and particle swarm optimization-support vector machine","volume":"42","author":"Wang","year":"2021","journal-title":"Int. J. Coal Prep. Util."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dong, Z., Zheng, J., Huang, S., Pan, H., and Liu, Q. (2019). Time-Shift Multi-Scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing. Entropy, 21.","DOI":"10.3390\/e21060621"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hou, Y., Gao, H., Wang, Z., and Du, C. (2022). Improved Grey Wolf Optimization Algorithm and Application. Sensors, 22.","DOI":"10.3390\/s22103810"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1007\/s00366-019-00795-0","article-title":"Enhanced leadership-inspired grey wolf optimizer for global optimization problems","volume":"36","author":"Gupta","year":"2020","journal-title":"Eng. Comput."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/1\/202\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:17Z","timestamp":1760119517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/1\/202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["sym15010202"],"URL":"https:\/\/doi.org\/10.3390\/sym15010202","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}