Detection method of coal quantity and deviation of belt conveyor based on image recognitio
Traditional convolutional neural network（CNN） is a single-task network. In order to realize simultaneous detection of coal quantity and deviation of belt conveyor, two CNNs are used to detect coal quantity and deviation respectively, resulting in large network volume, many parameters, large computat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2020-04-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019080055 |
| _version_ | 1818720714769825792 |
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| author | HAN Tao HUANG Yourui ZHANG Lizhi XU Shanyong XU Jiachang BAO Shishui |
| author_facet | HAN Tao HUANG Yourui ZHANG Lizhi XU Shanyong XU Jiachang BAO Shishui |
| author_sort | HAN Tao |
| collection | DOAJ |
| description | Traditional convolutional neural network（CNN） is a single-task network. In order to realize simultaneous detection of coal quantity and deviation of belt conveyor, two CNNs are used to detect coal quantity and deviation respectively, resulting in large network volume, many parameters, large computation and long operation time, which seriously affect detection performance. In order to reduce complexity of network structure, a detection method of coal quantity and deviation of belt conveyor based on multi-task convolutional neural network (MT-CNN) was proposed, which could make two tasks of coal quantity detection and deviation detection to share the same network underlying structure and parameters. On the basis of VGGNet model, MT-CNN is constructed by increasing scale of convolution kernel and pooling kernel, reducing the number of channels in full connection layer, and changing structure of output layer. After preprocessing the acquired conveyor belt images, such as graying, median filtering and extracting region of interest, the training dataset and test dataset are acquired, and the MT-CNN is trained. The trained MT-CNN is used to identify and classify the conveyor belt images, so as to realize accurate and fast detection of coal quantity and deviation. The experimental results show that detection accuracy of the trained MT-CNN in the test dataset is 97.3%, and average processing time of each image is about 23.1 ms. The effectiveness of the method is verified by field operation. |
| first_indexed | 2024-12-17T20:27:14Z |
| format | Article |
| id | doaj.art-4f24495828a24782b4004f336b1cd1b7 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-12-17T20:27:14Z |
| publishDate | 2020-04-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-4f24495828a24782b4004f336b1cd1b72022-12-21T21:33:43ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2020-04-01464172210.13272/j.issn.1671-251x.2019080055Detection method of coal quantity and deviation of belt conveyor based on image recognitioHAN TaoHUANG YouruiZHANG LizhiXU ShanyongXU JiachangBAO ShishuiTraditional convolutional neural network（CNN） is a single-task network. In order to realize simultaneous detection of coal quantity and deviation of belt conveyor, two CNNs are used to detect coal quantity and deviation respectively, resulting in large network volume, many parameters, large computation and long operation time, which seriously affect detection performance. In order to reduce complexity of network structure, a detection method of coal quantity and deviation of belt conveyor based on multi-task convolutional neural network (MT-CNN) was proposed, which could make two tasks of coal quantity detection and deviation detection to share the same network underlying structure and parameters. On the basis of VGGNet model, MT-CNN is constructed by increasing scale of convolution kernel and pooling kernel, reducing the number of channels in full connection layer, and changing structure of output layer. After preprocessing the acquired conveyor belt images, such as graying, median filtering and extracting region of interest, the training dataset and test dataset are acquired, and the MT-CNN is trained. The trained MT-CNN is used to identify and classify the conveyor belt images, so as to realize accurate and fast detection of coal quantity and deviation. The experimental results show that detection accuracy of the trained MT-CNN in the test dataset is 97.3%, and average processing time of each image is about 23.1 ms. The effectiveness of the method is verified by field operation.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019080055belt conveyorcoal quantity detectiondeviation detectionimage recognitionmulti-task convolutional neural network |
| spellingShingle | HAN Tao HUANG Yourui ZHANG Lizhi XU Shanyong XU Jiachang BAO Shishui Detection method of coal quantity and deviation of belt conveyor based on image recognitio Gong-kuang zidonghua belt conveyor coal quantity detection deviation detection image recognition multi-task convolutional neural network |
| title | Detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| title_full | Detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| title_fullStr | Detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| title_full_unstemmed | Detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| title_short | Detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| title_sort | detection method of coal quantity and deviation of belt conveyor based on image recognitio |
| topic | belt conveyor coal quantity detection deviation detection image recognition multi-task convolutional neural network |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019080055 |
| work_keys_str_mv | AT hantao detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio AT huangyourui detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio AT zhanglizhi detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio AT xushanyong detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio AT xujiachang detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio AT baoshishui detectionmethodofcoalquantityanddeviationofbeltconveyorbasedonimagerecognitio |