Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field
Abstract
:1. Introduction
2. Results and Discussion
2.1. Experimental Platform
2.2. Performance Evaluation of Models
2.3. Ablation Experiments
2.4. Comparison Experiments
2.5. Visual Analysis of Test Results
3. Materials and Methods
3.1. Cotton Weed Dataset
3.1.1. Data Selection
3.1.2. Image Augmentation
3.2. YOLOv8 Model
3.3. Improved YOLOv8-DMAS
3.3.1. DWR Module
3.3.2. Multi-Scale Block
3.3.3. Improved Detection Head
3.3.4. Soft-NMS Method
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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D | M | A | S | P (%) | R (%) | mAP0.5 (%) | mAP0.5:0.95 (%) | Params (M) | GFlops (G) |
---|---|---|---|---|---|---|---|---|---|
× | × | × | × | 94.1 | 88.4 | 93.4 | 88.4 | 11.2 | 28.6 |
√ | × | × | × | 94.9 | 87.5 | 93.9 | 88.9 | 10.6 | 27.4 |
√ | √ | × | × | 94.3 | 89.6 | 94.7 | 89.7 | 13.2 | 29.5 |
√ | √ | √ | × | 95.3 | 91.1 | 94.5 | 91.4 | 19.03 | 51.2 |
√ | √ | √ | √ | 95.8 | 92.2 | 95.5 | 92.1 | 19.03 | 51.2 |
Algorithm | Parameters (M) | P (%) | R (%) | [email protected] (%) | [email protected]:0.95 (%) | Calculations (GFLOPs) | FPS (ms/Frame) |
---|---|---|---|---|---|---|---|
YOLOv8s | 11.13 | 94.1 | 88.4 | 93.4 | 88.4 | 28.4 | 85.67 |
YOLOv3 | 61.583 | 88.4 | 88.2 | 91.5 | 83.6 | 154.7 | 80.65 |
YOLOv4 Tiny | 6.057 | 94.1 | 79.00 | 89.6 | 61.7 | 6.9 | 92.18 |
YOLOv5s | 7.023 | 93.9 | 88.2 | 93.7 | 87.3 | 15.8 | 85.19 |
YOLOv7 | 37.256 | 94.2 | 88.0 | 93.4 | 87.6 | 105.3 | 74.07 |
YOLOv8l | 43.64 | 94.5 | 88.8 | 94.0 | 89.7 | 165.5 | 73.69 |
SSD | 50.21 | 83.4 | 86.6 | 89.6 | 49.2 | 360.7 | 47.938 |
Faster R-CNN | 137.09 | 77.6 | 95.1 | 94.74 | 74.3 | 370.2 | 10.262 |
Ours | 19.03 | 95.8 | 92.2 | 95.5 | 92.1 | 51.2 | 82.47 |
Dataset Category | Division Ratio | Sample Size | Augmented |
---|---|---|---|
Training set | 70% | 7906 | √ |
Validation set | 15% | 847 | × |
Test set | 15% | 848 | × |
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Zheng, L.; Yi, J.; He, P.; Tie, J.; Zhang, Y.; Wu, W.; Long, L. Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field. Plants 2024, 13, 1843. https://doi.org/10.3390/plants13131843
Zheng L, Yi J, He P, Tie J, Zhang Y, Wu W, Long L. Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field. Plants. 2024; 13(13):1843. https://doi.org/10.3390/plants13131843
Chicago/Turabian StyleZheng, Lu, Junchao Yi, Pengcheng He, Jun Tie, Yibo Zhang, Weibo Wu, and Lyujia Long. 2024. "Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field" Plants 13, no. 13: 1843. https://doi.org/10.3390/plants13131843