Coal-gangue image recognition in fully-mechanized caving face based on random forest
Aiming at problems of high difficulty in parameter adjustment, low prediction accuracy and easy over-fitting in present coal-gangue image recognition methods in fully-mechanized caving face, a coal-gangue image recognition method in fully mechanized caving face based on random forest (RF) algorithm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2020-05-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019110064 |
| _version_ | 1831582579051986944 |
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| author | XUE Guanghui LI Xiuying QIAN Xiaoling ZHANG Yunfei |
| author_facet | XUE Guanghui LI Xiuying QIAN Xiaoling ZHANG Yunfei |
| author_sort | XUE Guanghui |
| collection | DOAJ |
| description | Aiming at problems of high difficulty in parameter adjustment, low prediction accuracy and easy over-fitting in present coal-gangue image recognition methods in fully-mechanized caving face, a coal-gangue image recognition method in fully mechanized caving face based on random forest (RF) algorithm is proposed. Taking 6203 fully-mechanized caving face of Danshuigou Coal Mine as project background, coal-gangue image of caving mouth are collected and pre-processed by clipping, gray conversion, contrast enhancement and image filtering. Fifteen texture features of coal-gangue image are extracted by gray-gradient co-occurrence matrix. RF algorithm is used to rank the importance of the fifteen coal-gangue texture features, and the first five features are selected for dimension reduction. Recognition effect of RF algorithm on coal-gangue images before and after dimension reduction is analyzed. The results show that when the number of decision tree is 150 and the number of features in each split is calculated by logM2+1 method, accuracy rate of coal-gangue classification of RF model after dimension reduction is 97%, which is 4% higher than that before dimension reduction, accuracy rate coal-gangue classification is 0.98, recall rate is 0.96, and out-of-bag error rate reaches 9% after 50 iterations with stronger generalization. |
| first_indexed | 2024-12-17T20:27:07Z |
| format | Article |
| id | doaj.art-21a2355689a54e1eafaeb0c78a3d1568 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-12-17T20:27:07Z |
| publishDate | 2020-05-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-21a2355689a54e1eafaeb0c78a3d15682022-12-21T21:33:43ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2020-05-01465576210.13272/j.issn.1671-251x.2019110064Coal-gangue image recognition in fully-mechanized caving face based on random forestXUE GuanghuiLI XiuyingQIAN XiaolingZHANG YunfeiAiming at problems of high difficulty in parameter adjustment, low prediction accuracy and easy over-fitting in present coal-gangue image recognition methods in fully-mechanized caving face, a coal-gangue image recognition method in fully mechanized caving face based on random forest (RF) algorithm is proposed. Taking 6203 fully-mechanized caving face of Danshuigou Coal Mine as project background, coal-gangue image of caving mouth are collected and pre-processed by clipping, gray conversion, contrast enhancement and image filtering. Fifteen texture features of coal-gangue image are extracted by gray-gradient co-occurrence matrix. RF algorithm is used to rank the importance of the fifteen coal-gangue texture features, and the first five features are selected for dimension reduction. Recognition effect of RF algorithm on coal-gangue images before and after dimension reduction is analyzed. The results show that when the number of decision tree is 150 and the number of features in each split is calculated by logM2+1 method, accuracy rate of coal-gangue classification of RF model after dimension reduction is 97%, which is 4% higher than that before dimension reduction, accuracy rate coal-gangue classification is 0.98, recall rate is 0.96, and out-of-bag error rate reaches 9% after 50 iterations with stronger generalization.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019110064fully-mechanized caving facecoal-gangue image recognitiontexture feature of coal- gangue imageautomatic recognition of falling coal-ganguerandom forest algorithm |
| spellingShingle | XUE Guanghui LI Xiuying QIAN Xiaoling ZHANG Yunfei Coal-gangue image recognition in fully-mechanized caving face based on random forest Gong-kuang zidonghua fully-mechanized caving face coal-gangue image recognition texture feature of coal- gangue image automatic recognition of falling coal-gangue random forest algorithm |
| title | Coal-gangue image recognition in fully-mechanized caving face based on random forest |
| title_full | Coal-gangue image recognition in fully-mechanized caving face based on random forest |
| title_fullStr | Coal-gangue image recognition in fully-mechanized caving face based on random forest |
| title_full_unstemmed | Coal-gangue image recognition in fully-mechanized caving face based on random forest |
| title_short | Coal-gangue image recognition in fully-mechanized caving face based on random forest |
| title_sort | coal gangue image recognition in fully mechanized caving face based on random forest |
| topic | fully-mechanized caving face coal-gangue image recognition texture feature of coal- gangue image automatic recognition of falling coal-gangue random forest algorithm |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019110064 |
| work_keys_str_mv | AT xueguanghui coalgangueimagerecognitioninfullymechanizedcavingfacebasedonrandomforest AT lixiuying coalgangueimagerecognitioninfullymechanizedcavingfacebasedonrandomforest AT qianxiaoling coalgangueimagerecognitioninfullymechanizedcavingfacebasedonrandomforest AT zhangyunfei coalgangueimagerecognitioninfullymechanizedcavingfacebasedonrandomforest |