Method for recognizing coal flow status of scraper conveyor in working face
The various poses of scraper conveyors, irregular coal material shapes, limited equipment installation positions, high dust, and foreign object obstruction in the scene of scraper conveyors in underground coal mines have led to the inability of existing coal flow status recognition methods for belt...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2023-11-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2023080101 |
| _version_ | 1797361089321631744 |
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| author | WU Jiangwei NAN Bingfei |
| author_facet | WU Jiangwei NAN Bingfei |
| author_sort | WU Jiangwei |
| collection | DOAJ |
| description | The various poses of scraper conveyors, irregular coal material shapes, limited equipment installation positions, high dust, and foreign object obstruction in the scene of scraper conveyors in underground coal mines have led to the inability of existing coal flow status recognition methods for belt conveyor scenarios to be applied in engineering. In order to solve the above problems, a method for recognizing the coal flow status of a scraper conveyor in a working face based on temporal visual features is proposed. This method first utilizes the DeepLabV3+semantic segmentation model to obtain rough coal flow regions in the coal flow video image of the working face. Then the method uses linear fitting method to locate and segment fine coal flow regions, achieving coal flow image extraction. Then the method arranges the coal flow images in video sequence to form a sequence of coal flow images. Finally, a convolutional 3D (C3D) action recognition model is used to model the features of coal flow image sequences and achieve automatic recognition of coal flow status. The experimental results show that this method can accurately obtain coal flow images and automatically and real-time recognize coal flow status, with an average recognition accuracy of 92.73% for coal flow status. For engineering deployment applications, TensorRT is used to accelerate model processing. For the coal flow video image with a resolution of 1 280×720, the overall processing speed is 42.7 frames/s, which meets the actual demand for intelligent monitoring of coal flow status at the working face. |
| first_indexed | 2024-03-08T15:48:56Z |
| format | Article |
| id | doaj.art-9a31d292a92745d795101fc4c9410882 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-03-08T15:48:56Z |
| publishDate | 2023-11-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-9a31d292a92745d795101fc4c94108822024-01-09T08:36:54ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2023-11-014911606610.13272/j.issn.1671-251x.2023080101Method for recognizing coal flow status of scraper conveyor in working faceWU JiangweiNAN BingfeiThe various poses of scraper conveyors, irregular coal material shapes, limited equipment installation positions, high dust, and foreign object obstruction in the scene of scraper conveyors in underground coal mines have led to the inability of existing coal flow status recognition methods for belt conveyor scenarios to be applied in engineering. In order to solve the above problems, a method for recognizing the coal flow status of a scraper conveyor in a working face based on temporal visual features is proposed. This method first utilizes the DeepLabV3+semantic segmentation model to obtain rough coal flow regions in the coal flow video image of the working face. Then the method uses linear fitting method to locate and segment fine coal flow regions, achieving coal flow image extraction. Then the method arranges the coal flow images in video sequence to form a sequence of coal flow images. Finally, a convolutional 3D (C3D) action recognition model is used to model the features of coal flow image sequences and achieve automatic recognition of coal flow status. The experimental results show that this method can accurately obtain coal flow images and automatically and real-time recognize coal flow status, with an average recognition accuracy of 92.73% for coal flow status. For engineering deployment applications, TensorRT is used to accelerate model processing. For the coal flow video image with a resolution of 1 280×720, the overall processing speed is 42.7 frames/s, which meets the actual demand for intelligent monitoring of coal flow status at the working face.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2023080101scraper conveyorrecognition of coal flow statustemporal visual featuressemantic segmentationaction recognition |
| spellingShingle | WU Jiangwei NAN Bingfei Method for recognizing coal flow status of scraper conveyor in working face Gong-kuang zidonghua scraper conveyor recognition of coal flow status temporal visual features semantic segmentation action recognition |
| title | Method for recognizing coal flow status of scraper conveyor in working face |
| title_full | Method for recognizing coal flow status of scraper conveyor in working face |
| title_fullStr | Method for recognizing coal flow status of scraper conveyor in working face |
| title_full_unstemmed | Method for recognizing coal flow status of scraper conveyor in working face |
| title_short | Method for recognizing coal flow status of scraper conveyor in working face |
| title_sort | method for recognizing coal flow status of scraper conveyor in working face |
| topic | scraper conveyor recognition of coal flow status temporal visual features semantic segmentation action recognition |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2023080101 |
| work_keys_str_mv | AT wujiangwei methodforrecognizingcoalflowstatusofscraperconveyorinworkingface AT nanbingfei methodforrecognizingcoalflowstatusofscraperconveyorinworkingface |