Detection of Coal and Gangue Based on Improved YOLOv8
To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1246 |
| _version_ | 1797297008441032704 |
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| author | Qingliang Zeng Guangyu Zhou Lirong Wan Liang Wang Guantao Xuan Yuanyuan Shao |
| author_facet | Qingliang Zeng Guangyu Zhou Lirong Wan Liang Wang Guantao Xuan Yuanyuan Shao |
| author_sort | Qingliang Zeng |
| collection | DOAJ |
| description | To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications. |
| first_indexed | 2024-03-07T22:15:01Z |
| format | Article |
| id | doaj.art-78d82184f0ed47cf835286fe07d4a7cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:01Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-78d82184f0ed47cf835286fe07d4a7cf2024-02-23T15:33:57ZengMDPI AGSensors1424-82202024-02-01244124610.3390/s24041246Detection of Coal and Gangue Based on Improved YOLOv8Qingliang Zeng0Guangyu Zhou1Lirong Wan2Liang Wang3Guantao Xuan4Yuanyuan Shao5College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, ChinaCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, ChinaTo address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.https://www.mdpi.com/1424-8220/24/4/1246coalgangueidentificationYOLOv8lightweight |
| spellingShingle | Qingliang Zeng Guangyu Zhou Lirong Wan Liang Wang Guantao Xuan Yuanyuan Shao Detection of Coal and Gangue Based on Improved YOLOv8 Sensors coal gangue identification YOLOv8 lightweight |
| title | Detection of Coal and Gangue Based on Improved YOLOv8 |
| title_full | Detection of Coal and Gangue Based on Improved YOLOv8 |
| title_fullStr | Detection of Coal and Gangue Based on Improved YOLOv8 |
| title_full_unstemmed | Detection of Coal and Gangue Based on Improved YOLOv8 |
| title_short | Detection of Coal and Gangue Based on Improved YOLOv8 |
| title_sort | detection of coal and gangue based on improved yolov8 |
| topic | coal gangue identification YOLOv8 lightweight |
| url | https://www.mdpi.com/1424-8220/24/4/1246 |
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