Detection of Famous Tea Buds Based on Improved YOLOv7 Network
Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/6/1190 |
| _version_ | 1797596617485844480 |
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| author | Yongwei Wang Maohua Xiao Shu Wang Qing Jiang Xiaochan Wang Yongnian Zhang |
| author_facet | Yongwei Wang Maohua Xiao Shu Wang Qing Jiang Xiaochan Wang Yongnian Zhang |
| author_sort | Yongwei Wang |
| collection | DOAJ |
| description | Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea. |
| first_indexed | 2024-03-11T02:52:42Z |
| format | Article |
| id | doaj.art-4b35b659da2a40f48a96567189a39f5a |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T02:52:42Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-4b35b659da2a40f48a96567189a39f5a2023-11-18T08:51:35ZengMDPI AGAgriculture2077-04722023-06-01136119010.3390/agriculture13061190Detection of Famous Tea Buds Based on Improved YOLOv7 NetworkYongwei Wang0Maohua Xiao1Shu Wang2Qing Jiang3Xiaochan Wang4Yongnian Zhang5Engineering College, Nanjing Agricultural University, Nanjing 210031, ChinaEngineering College, Nanjing Agricultural University, Nanjing 210031, ChinaEngineering College, Nanjing Agricultural University, Nanjing 210031, ChinaEngineering College, Nanjing Agricultural University, Nanjing 210031, ChinaEngineering College, Nanjing Agricultural University, Nanjing 210031, ChinaEngineering College, Nanjing Agricultural University, Nanjing 210031, ChinaAiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea.https://www.mdpi.com/2077-0472/13/6/1190famous and excellent green teabud detectionimproved YOLOv7 algorithmattention mechanics |
| spellingShingle | Yongwei Wang Maohua Xiao Shu Wang Qing Jiang Xiaochan Wang Yongnian Zhang Detection of Famous Tea Buds Based on Improved YOLOv7 Network Agriculture famous and excellent green tea bud detection improved YOLOv7 algorithm attention mechanics |
| title | Detection of Famous Tea Buds Based on Improved YOLOv7 Network |
| title_full | Detection of Famous Tea Buds Based on Improved YOLOv7 Network |
| title_fullStr | Detection of Famous Tea Buds Based on Improved YOLOv7 Network |
| title_full_unstemmed | Detection of Famous Tea Buds Based on Improved YOLOv7 Network |
| title_short | Detection of Famous Tea Buds Based on Improved YOLOv7 Network |
| title_sort | detection of famous tea buds based on improved yolov7 network |
| topic | famous and excellent green tea bud detection improved YOLOv7 algorithm attention mechanics |
| url | https://www.mdpi.com/2077-0472/13/6/1190 |
| work_keys_str_mv | AT yongweiwang detectionoffamousteabudsbasedonimprovedyolov7network AT maohuaxiao detectionoffamousteabudsbasedonimprovedyolov7network AT shuwang detectionoffamousteabudsbasedonimprovedyolov7network AT qingjiang detectionoffamousteabudsbasedonimprovedyolov7network AT xiaochanwang detectionoffamousteabudsbasedonimprovedyolov7network AT yongnianzhang detectionoffamousteabudsbasedonimprovedyolov7network |