Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3
Abstract The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recogn...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12339 |
| _version_ | 1797994935257923584 |
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| author | De‐yong Li Guo‐fa Wang Yong Zhang Shuang Wang |
| author_facet | De‐yong Li Guo‐fa Wang Yong Zhang Shuang Wang |
| author_sort | De‐yong Li |
| collection | DOAJ |
| description | Abstract The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue. |
| first_indexed | 2024-04-11T09:53:39Z |
| format | Article |
| id | doaj.art-0d845d3852114c1cb1051041c2862c13 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T09:53:39Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-0d845d3852114c1cb1051041c2862c132022-12-22T04:30:43ZengWileyIET Image Processing1751-96591751-96672022-01-0116113414410.1049/ipr2.12339Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3De‐yong Li0Guo‐fa Wang1Yong Zhang2Shuang Wang3State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaChina Coal Technology Engineering Group Coal Mining Research Institute Beijing ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaAbstract The intelligentisation of coal mines is the only approach to the high‐quality development of the coal industry. Detection, identification and sorting of coal gangue is an important part of the intelligentisation of coal mines. Focusing on various problems in coal gangue detecting and recognising algorithms, such as limited receptive field, slow convergence rate and low accuracy of small particle recognition, this paper proposes a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN‐YOLOv3). To improve the accuracy of anchor frame positioning and enhance the diversity of the dataset, the deformed convolution YOLOv3 network model is established based on the detection algorithm YOLOv3, using deformable convolution, multiple k‐means clustering results average method and data enhancement technology as means. The model was trained through the self‐designed dataset, and the algorithm's correctness and accuracy for coal gangue recognition under different size and illumination conditions are verified. The test results showed that the algorithm effectively detects and recognises coal gangue, improves the accuracy and efficiency of detecting and recognising small‐size coal and gangue and improves environmental robustness. Furthermore, compared with the traditional recognition algorithm, the network convergence speed of this algorithm is significantly improved, the mAP is increased to 99.45%, and the maximum FLOPs value is reduced by 61.4%. Accordingly, this research is considered to be of certain theoretical value and technical reference for identifying coal gangue.https://doi.org/10.1049/ipr2.12339Data handling techniquesProduction engineering computingIndustrial applications of ITMining, oil drilling and natural gas industriesNeural netscoal gangue recognition |
| spellingShingle | De‐yong Li Guo‐fa Wang Yong Zhang Shuang Wang Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 IET Image Processing Data handling techniques Production engineering computing Industrial applications of IT Mining, oil drilling and natural gas industries Neural nets coal gangue recognition |
| title | Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 |
| title_full | Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 |
| title_fullStr | Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 |
| title_full_unstemmed | Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 |
| title_short | Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 |
| title_sort | coal gangue detection and recognition algorithm based on deformable convolution yolov3 |
| topic | Data handling techniques Production engineering computing Industrial applications of IT Mining, oil drilling and natural gas industries Neural nets coal gangue recognition |
| url | https://doi.org/10.1049/ipr2.12339 |
| work_keys_str_mv | AT deyongli coalganguedetectionandrecognitionalgorithmbasedondeformableconvolutionyolov3 AT guofawang coalganguedetectionandrecognitionalgorithmbasedondeformableconvolutionyolov3 AT yongzhang coalganguedetectionandrecognitionalgorithmbasedondeformableconvolutionyolov3 AT shuangwang coalganguedetectionandrecognitionalgorithmbasedondeformableconvolutionyolov3 |