## 3 Results
### Model Performance and Comparison
#### 3.3.3 Comparison of Different Mainstream Models  
To validate the performance of the proposed COTTON-YOLO algorithm in cotton bolt detection applications, we compared it with several popular current object detection models. These models include various versions of YOLO and architectures derived from DETR. The evaluation is primarily based on key performance indicators, including precision, recall, \(AP_{50}\), and \(AP_{50.95}\), while also considering efficiency metrics such as the number of parameters and model size. The specific model comparison experimental results are shown  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & \multirow{2}{*}{**C2\(f\)-CBAM**} & \multirow{2}{*}{**GD Neck**} & \multirow{2}{*}{**WloU v3**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**\(AP_{50}\)**} & \multirow{2}{*}{**\(AP_{50.95}\)**} \\
**Enhancement** & & & & & & 83.3 & 79.8 & 86.8 & 50.9 \\ \hline \(\surd\) & & & & & 87.3 & 84.8 & 92.0 & 59.4 \\ \(\surd\) & \(\surd\) & & & & 89.8 & 87.0 & 93.0 & 59.7 \\ \(\surd\) & & \(\surd\) & & & 89.0 & 86.2 & 92.5 & 61.0 \\ \(\surd\) & & & \(\surd\) & & 91.2 & 85.1 & 92.2 & 60.7 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 92.1 & 84.4 & 93.1 & 62.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 94.1 & 91.3 & 95.8 & 65.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment.  
Figure 13: Comparison of models under different weather conditions: (**a**,**d**) are clear weather; (**b**,**e**) are cloudy; (**c**,**f**) are rainy. (**a**–**c**) use YOLOv8, while (**d**–**f**) use COTTON-YOLO.  
in Table 5. YOLOv3-tiny and YOLOv5s are lightweight models; despite having fewer parameters, their performance on \(AP_{50:95}\) (53.5% and 53.8%, respectively) does not reach that of COTTON-YOLO. YOLOv6s and YOLOv8m performed well in terms of precision and recall, but COTTON-YOLO excelled in \(AP_{50:95}\) with a score of 65.7%, demonstrating better overall performance. The YOLOv8-DETR models, based on DETR [31], despite their innovative structures, fell short in all performance metrics compared to COTTON-YOLO, particularly in \(AP_{50:95}\), with scores of 42.6% and 43.1%, respectively. YOLOv9-gelan performed excellently among the comparison models, but COTTON-YOLO still outperformed it in \(AP_{50}\) and \(AP_{50:95}\) metrics, despite YOLOv9-gelan's larger model size. COTTON-YOLO exhibited outstanding detection capabilities, particularly in \(AP_{50:95}\), achieving 65.7%, significantly higher than other models, while maintaining a low parameter count (6.1 M) and a small model size (12.4 MB). This result demonstrates that COTTON-YOLO has significant advantages in both accuracy and the ability to handle complex backgrounds when processing the specific task of cotton bolt detection. The comparative analysis clearly illustrates the significant advantages of COTTON-YOLO over existing detection models in terms of accuracy, efficiency, and applicability. Its high precision, high recall rate, and excellent \(AP_{50:95}\) performance make it particularly suitable for real-time cotton bolt detection tasks in the agricultural field, providing stable and reliable performance in dynamic agricultural environments.