V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments
Abstract
:1. Introduction
2. Materials and Methods
2.1. Image Acquisition
2.2. Data Set Making and Partitioning
2.3. Standard YOLOv10 Model
2.4. V-YOLO
2.4.1. Backbone
2.4.2. Attention Mechanism
2.4.3. Head
2.5. Model Evaluation Metrics
2.6. Experiment Settings
3. Result
3.1. Ablation Experiments
3.2. Guava Detection Performance
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Total | Training Set | Verification Set | Test Set | |
---|---|---|---|---|
Images of large fruit | 510 | 351 | 81 | 78 |
Images of small fruit | 580 | 412 | 82 | 86 |
Total | 1090 | 763 | 163 | 164 |
Modified Part | Parameters | FLOPs | FPS | P | R | mAP | ||
---|---|---|---|---|---|---|---|---|
Backbone | SimAM | Head | ||||||
× | × | × | 2,695,196 | 8.2 G | 625 | 90.0% | 89.9% | 95.0% |
√ | × | × | 1,147,684 | 4.1 G | 1000 | 89.1% | 89.0% | 93.7% |
√ | √ | × | 1,147,684 | 4.1 G | 1000 | 91.0% | 86.7% | 93.8% |
√ | √ | √ | 1,165,118 | 3.4 G | 1666 | 93.3% | 89.3% | 95.0% |
Model | Parameters | FLOPs | FPS | P | R | mAP |
---|---|---|---|---|---|---|
YOLOv10n | 2,695,196 | 8.2 G | 625 | 90.0% | 89.9% | 95.0% |
YOLOv10s | 8,036,508 | 24.4 G | 400 | 90.7% | 90.1% | 94.9% |
YOLOv10m | 16,452,700 | 63.4 G | 192 | 89.9% | 90.4% | 93.6% |
YOLOv10b | 20,414,236 | 97.9 G | 149 | 92.8% | 87.4% | 94.2% |
YOLOv10l | 25,719,452 | 126.3 G | 122 | 90.7% | 87.9% | 93.9% |
YOLOv10x | 31,587,932 | 169.8 G | 79. | 91.6% | 88.0% | 94.1% |
RT-DETR-l | 31,987,850 | 103.4 G | 135 | 87.6% | 85.1% | 91.6% |
RT-DETR-R50 | 41,938,794 | 125.6 G | 114 | 84.4% | 81.7% | 88.1% |
RT-DETR-R101 | 60,904,810 | 186.2 G | 75 | 87.0% | 85.9% | 91.2% |
RT-DETR-x | 65,471,546 | 222.5 G | 83 | 83.9% | 86.0% | 90.6% |
V-YOLO | 1,165,118 | 3.4 G | 1666 | 93.3% | 89.3% | 95.0% |
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Liu, Z.; Xiong, J.; Cai, M.; Li, X.; Tan, X. V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments. Agronomy 2024, 14, 1988. https://doi.org/10.3390/agronomy14091988
Liu Z, Xiong J, Cai M, Li X, Tan X. V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments. Agronomy. 2024; 14(9):1988. https://doi.org/10.3390/agronomy14091988
Chicago/Turabian StyleLiu, Zhen, Juntao Xiong, Mingrui Cai, Xiaoxin Li, and Xinjie Tan. 2024. "V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments" Agronomy 14, no. 9: 1988. https://doi.org/10.3390/agronomy14091988