Research on Recognition of Coal and Gangue Based on Laser Speckle Images
Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/22/9113 |
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| author | Hequn Li Qiong Wang Ling Ling Ziqi Lv Yun Liu Mingxing Jiao |
| author_facet | Hequn Li Qiong Wang Ling Ling Ziqi Lv Yun Liu Mingxing Jiao |
| author_sort | Hequn Li |
| collection | DOAJ |
| description | Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines. |
| first_indexed | 2024-03-09T16:28:28Z |
| format | Article |
| id | doaj.art-6c10a700f36744cf9653fb0b2defc764 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:28Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6c10a700f36744cf9653fb0b2defc7642023-11-24T15:05:23ZengMDPI AGSensors1424-82202023-11-012322911310.3390/s23229113Research on Recognition of Coal and Gangue Based on Laser Speckle ImagesHequn Li0Qiong Wang1Ling Ling2Ziqi Lv3Yun Liu4Mingxing Jiao5School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Chemical and Environmental Engineering, China University of Mining & Technology-Beijing, Beijing 100083, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCoal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.https://www.mdpi.com/1424-8220/23/22/9113coal gangue recognitionlaser specklegray featuretexture featureilluminance |
| spellingShingle | Hequn Li Qiong Wang Ling Ling Ziqi Lv Yun Liu Mingxing Jiao Research on Recognition of Coal and Gangue Based on Laser Speckle Images Sensors coal gangue recognition laser speckle gray feature texture feature illuminance |
| title | Research on Recognition of Coal and Gangue Based on Laser Speckle Images |
| title_full | Research on Recognition of Coal and Gangue Based on Laser Speckle Images |
| title_fullStr | Research on Recognition of Coal and Gangue Based on Laser Speckle Images |
| title_full_unstemmed | Research on Recognition of Coal and Gangue Based on Laser Speckle Images |
| title_short | Research on Recognition of Coal and Gangue Based on Laser Speckle Images |
| title_sort | research on recognition of coal and gangue based on laser speckle images |
| topic | coal gangue recognition laser speckle gray feature texture feature illuminance |
| url | https://www.mdpi.com/1424-8220/23/22/9113 |
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