## 3 Results
### Model Performance and Comparison
#### 3.3.2 Ablation Experiment  
This study on the COTTON-YOLO model included a series of ablation experiments to evaluate the impact of various data augmentation techniques and architectural improvements on model performance. Figure 13 depicts the comparison between COTTON-YOLO and the baseline model YOLOv8 in detecting cotton bolls under various weather conditions. In clear weather conditions, both YOLOv8 and COTTON-YOLO successfully detected all the cotton bolls. Under overcast conditions, YOLOv8 failed to detect some cotton bolls at the edges of the image, whereas COTTON-YOLO successfully detected all the cotton bolls. In rainy conditions, YOLOv8 failed to detect the smaller cotton bolls. COTTON-YOLO exhibits superior confidence levels in the detection of targets, exhibiting neither misses nor false positives, thus ensuring high accuracy and reliability in its operational performance. In contrast, YOLOv8, when analyzing the same images, encountered challenges related to false positives, thereby reducing its precision. Consequently, considering the critical assessment of accuracy, reliability, and the incidence of false detections, it is evident that COTTON-YOLO substantially surpasses YOLOv8 in the detection efficacy of cotton bolls.  
Table 4 details the results of these experiments, highlighting the specific contributions of different configuration combinations to precision and recall. A checkmark (\(\surd\)) denotes the methods or modules that were enabled. Implementing data augmentation methods, particularly padding techniques, increased \(AP_{50}\) from 86.8% to 92.0% and \(AP_{50.95}\) from 50.9% to 59.4%. This significant improvement demonstrates the effectiveness of this method in alleviating the difficulty of detecting small targets. Subsequently, by replacing the C2F bottleneck module with the CBAM module (C2F-CBAM), we further enhanced the model's multi-scale feature extraction capability, raising \(AP_{50}\) to 93.0% and \(AP_{50.95}\) to 59.7%. Replacing the standard neck part of YOLOv8 with the Gold YOLO neck additionally  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _IoU_ & **Precision** & **Recall** & \(AP_{50}\) & \(AP_{50.95}\) \\ \hline _CIoU_ & 87.3 & 84.8 & 92.0 & 59.4 \\ _DIoU_ & 87.7 & 82.9 & 91.9 & 55.4 \\ _EIoU_ & 85.3 & 83.1 & 91.2 & 54.8 \\ _SloU_ & 84.3 & 85.0 & 91.6 & 54.9 \\ _WIoU v1_ & 86.6 & 84.8 & 91.7 & 54.4 \\ _WIoU v2_ & 88.2 & 84.5 & 92.3 & 55.5 \\ _WIoU v3_ & 91.2 & 85.1 & 92.2 & 60.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of different _IoU_ methods.  
improved the model's performance, increasing \(AP_{50}\) from 92.0% to 92.5% and \(AP_{50.95}\) from 59.4% to 61.0%. Replacing the _CloU_ loss function in YOLOv8 with _WloU v3_ resulted in a final \(AP_{50}\) of 95.8% and \(AP_{50.95}\) of 65.7%. These results indicate that _WloU v3_ has excellent bounding box localization performance in object detection. With these improvements, the COTTON-YOLO model showed significant enhancements in precision, recall, and _AP_ metrics. These results not only demonstrate the effectiveness of individual improvements but also underscore their positive impact on model performance when used in combination. The figures illustrate the detection performance of the COTTON-YOLO model on small cotton bolt targets under different environmental conditions, thereby further validating its effectiveness in practical applications.