Research on Coal and Rock Type Recognition Based on Mechanical Vision
In order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6617717 |
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| _version_ | 1832567486811209728 |
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| author | Qiang Zhang Jieying Gu Junming Liu |
| author_facet | Qiang Zhang Jieying Gu Junming Liu |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | In order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based on the steerable pyramid decomposition (SPD). The maximum likelihood estimation method is used to estimate the parameters, and, the coal and rock types are judged by comparing the similarity of each image. The results show the following: (1) in the eight kinds of rocks, the recognition accuracy of shale and limestone is 90%, that of anorthosite is 95%, and those of other rocks are 100%; (2) the accuracy of comprehensive identification of coal, rock, and gangue is 93%, the comprehensive of coal and gangue is 78%, and the rock classification is 97%; (3) the identification time of 6 types of coal samples, 8 types of rock samples, and 2 types of coal gangue samples are in the range of 2 s∼3 s, which is far less than 10 s, which can meet the requirements of coal and rock identification in terms of recognition speed; and (4) according to 20 groups of data, the range, variance, and standard deviation of the same coal gangue sample meet the accuracy requirements of coal and rock identification. The identification method provides an effective method to improve the efficiency of coal separation, effectively determine the distribution of coal and rock, and timely adjust the cutting height of shearer drum and the operation parameters of various fully mechanized mining equipment, so as to improve the recovery rate of coal resources. |
| format | Article |
| id | doaj-art-efa66487a4c347b0934551d21f426c7d |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-efa66487a4c347b0934551d21f426c7d2025-02-03T01:01:25ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66177176617717Research on Coal and Rock Type Recognition Based on Mechanical VisionQiang Zhang0Jieying Gu1Junming Liu2School of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaSchool of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaSchool of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaIn order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based on the steerable pyramid decomposition (SPD). The maximum likelihood estimation method is used to estimate the parameters, and, the coal and rock types are judged by comparing the similarity of each image. The results show the following: (1) in the eight kinds of rocks, the recognition accuracy of shale and limestone is 90%, that of anorthosite is 95%, and those of other rocks are 100%; (2) the accuracy of comprehensive identification of coal, rock, and gangue is 93%, the comprehensive of coal and gangue is 78%, and the rock classification is 97%; (3) the identification time of 6 types of coal samples, 8 types of rock samples, and 2 types of coal gangue samples are in the range of 2 s∼3 s, which is far less than 10 s, which can meet the requirements of coal and rock identification in terms of recognition speed; and (4) according to 20 groups of data, the range, variance, and standard deviation of the same coal gangue sample meet the accuracy requirements of coal and rock identification. The identification method provides an effective method to improve the efficiency of coal separation, effectively determine the distribution of coal and rock, and timely adjust the cutting height of shearer drum and the operation parameters of various fully mechanized mining equipment, so as to improve the recovery rate of coal resources.http://dx.doi.org/10.1155/2021/6617717 |
| spellingShingle | Qiang Zhang Jieying Gu Junming Liu Research on Coal and Rock Type Recognition Based on Mechanical Vision Shock and Vibration |
| title | Research on Coal and Rock Type Recognition Based on Mechanical Vision |
| title_full | Research on Coal and Rock Type Recognition Based on Mechanical Vision |
| title_fullStr | Research on Coal and Rock Type Recognition Based on Mechanical Vision |
| title_full_unstemmed | Research on Coal and Rock Type Recognition Based on Mechanical Vision |
| title_short | Research on Coal and Rock Type Recognition Based on Mechanical Vision |
| title_sort | research on coal and rock type recognition based on mechanical vision |
| url | http://dx.doi.org/10.1155/2021/6617717 |
| work_keys_str_mv | AT qiangzhang researchoncoalandrocktyperecognitionbasedonmechanicalvision AT jieyinggu researchoncoalandrocktyperecognitionbasedonmechanicalvision AT junmingliu researchoncoalandrocktyperecognitionbasedonmechanicalvision |