TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements
Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we develo...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/547 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587498536042496 |
|---|---|
| author | Wenhui Fang Weizhen Chen |
| author_facet | Wenhui Fang Weizhen Chen |
| author_sort | Wenhui Fang |
| collection | DOAJ |
| description | Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model’s size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies. |
| format | Article |
| id | doaj-art-64536fc3077443189b1973d0d9c6d5eb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-64536fc3077443189b1973d0d9c6d5eb2025-01-24T13:49:18ZengMDPI AGSensors1424-82202025-01-0125254710.3390/s25020547TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n ImprovementsWenhui Fang0Weizhen Chen1School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, ChinaTea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model’s size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies.https://www.mdpi.com/1424-8220/25/2/547tea budsintelligenceYOLOv8ndistributed shift convolutioncomputer vision |
| spellingShingle | Wenhui Fang Weizhen Chen TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements Sensors tea buds intelligence YOLOv8n distributed shift convolution computer vision |
| title | TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements |
| title_full | TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements |
| title_fullStr | TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements |
| title_full_unstemmed | TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements |
| title_short | TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements |
| title_sort | tbf yolov8n a lightweight tea bud detection model based on yolov8n improvements |
| topic | tea buds intelligence YOLOv8n distributed shift convolution computer vision |
| url | https://www.mdpi.com/1424-8220/25/2/547 |
| work_keys_str_mv | AT wenhuifang tbfyolov8nalightweightteabuddetectionmodelbasedonyolov8nimprovements AT weizhenchen tbfyolov8nalightweightteabuddetectionmodelbasedonyolov8nimprovements |