Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network
To effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9684859/ |
| _version_ | 1819243111367311360 |
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| author | Feng Hu Kai Bian |
| author_facet | Feng Hu Kai Bian |
| author_sort | Feng Hu |
| collection | DOAJ |
| description | To effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared images without considering the selection of feature extraction and classifier. Firstly, the specific architecture and detailed parameters of the model are optimized and the CNN model based on only one Inception Block contains three different convolution kernels are considered to be the most appropriate model. Next, performance of the proposed identification model is analyzed and evaluated by the infrared image dataset, and we discovered that the CNN model is capable of correctly identifying 192 training samples and 48 test samples. Finally, compared with the traditional recognition model and other CNN recognition model, it is proved that the proposed CNN model has superior recognition performance. The results state clearly that the combination of infrared image and CNN can quickly and accurately identify coal and gangue without complex image processing steps. At the same time, the model has a certain anti-interference ability for different noises. And it has a certain reference value for the research and development of intelligent coal preparation equipment. |
| first_indexed | 2024-12-23T14:50:30Z |
| format | Article |
| id | doaj.art-9109e3f8d1ad4e4488b122b0dcc660be |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T14:50:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9109e3f8d1ad4e4488b122b0dcc660be2022-12-21T17:42:57ZengIEEEIEEE Access2169-35362022-01-01108758876610.1109/ACCESS.2022.31443869684859Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural NetworkFeng Hu0https://orcid.org/0000-0003-2088-6227Kai Bian1https://orcid.org/0000-0003-4231-6348School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaTo effectively separate coal and gangue, accurate classification is an important prerequisite. Here, a new recognition solution for coal and gangue is proposed, in which the convolutional neural network (CNN) is trained to achieve the automatically identifying coal and gangue based on the infrared images without considering the selection of feature extraction and classifier. Firstly, the specific architecture and detailed parameters of the model are optimized and the CNN model based on only one Inception Block contains three different convolution kernels are considered to be the most appropriate model. Next, performance of the proposed identification model is analyzed and evaluated by the infrared image dataset, and we discovered that the CNN model is capable of correctly identifying 192 training samples and 48 test samples. Finally, compared with the traditional recognition model and other CNN recognition model, it is proved that the proposed CNN model has superior recognition performance. The results state clearly that the combination of infrared image and CNN can quickly and accurately identify coal and gangue without complex image processing steps. At the same time, the model has a certain anti-interference ability for different noises. And it has a certain reference value for the research and development of intelligent coal preparation equipment.https://ieeexplore.ieee.org/document/9684859/Coal-gangue identificationconvolutional neural networkinfrared imaging technologyimage identification |
| spellingShingle | Feng Hu Kai Bian Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network IEEE Access Coal-gangue identification convolutional neural network infrared imaging technology image identification |
| title | Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network |
| title_full | Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network |
| title_fullStr | Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network |
| title_full_unstemmed | Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network |
| title_short | Accurate Identification Strategy of Coal and Gangue Using Infrared Imaging Technology Combined With Convolutional Neural Network |
| title_sort | accurate identification strategy of coal and gangue using infrared imaging technology combined with convolutional neural network |
| topic | Coal-gangue identification convolutional neural network infrared imaging technology image identification |
| url | https://ieeexplore.ieee.org/document/9684859/ |
| work_keys_str_mv | AT fenghu accurateidentificationstrategyofcoalandgangueusinginfraredimagingtechnologycombinedwithconvolutionalneuralnetwork AT kaibian accurateidentificationstrategyofcoalandgangueusinginfraredimagingtechnologycombinedwithconvolutionalneuralnetwork |