## 3 Results
### Ablation Experiment  
To validate the effectiveness of the WTConv, iRMB, and UIOU modules, a series of systematic ablation experiments were conducted under identical testing conditions, using YOLOv11n as the baseline model. The results are summarized in Table 4. The experimental results demonstrate that each proposed module contributes significantly to the performance improvement of SugarRow-YOLO. Specifically, the introduction of WTConv alone leads to noticeable enhancements in precision, recall, mAP\({}_{50}\), and mAP\({}_{50-95}\). This suggests that WTConv effectively improves the model's feature extraction capability by optimizing the convolutional structure. Second, a notable improvement in recall is observed when the iRMB attention mechanism is introduced independently. This enhancement is primarily attributed to the fine-grained feature weighting strategy of iRMB. It enables the model to better perceive object regions. This effect is especially evident in scenarios with densely distributed targets. By adopting UIOU as the regression loss function, the precision increases to 82.9% and the mAP\({}_{50-95}\) improves by 1.3%. These gains are attributed to UIOU's incorporation of dynamic weight allocation and a dual attention mechanism, which results in more accurate bounding box regression and significantly enhances target localization performance. The performance of the model is further enhanced when the two are used in combination. The synergistic effect of feature extraction enhancement and attention mechanism helps to improve the recall rate and optimize the detection accuracy; the combination of feature extraction and regression optimization excels in terms of localization accuracy and prediction frame stability; and the combination of the attention mechanism and high-quality regression strategy significantly enhances the overall detection performance, especially in the dense target detection task. These results indicate that the three modules complement each other effectively in terms of feature representation, target perception, and boundary regression, thereby significantly enhancing the overall detection capability of the model. In conclusion, the ablation study thoroughly validates the effectiveness and synergistic benefits of each proposed module in improving the performance of the SugarRow-YOLO model. This provides strong support for the subsequent modeling of crop rows in complex field environments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP\({}_{50}\)** & **mAP\({}_{50-95}\)** & **FLOPs(G)** & **FPS** \\ \hline YOLOv5n & 81\% & 87.8\% & 88.5\% & 65.7\% & 11.4 & 78.3 \\ YOLOv7-tiny & 80.3\% & 87.8\% & 88.1\% & 64.5\% & 21.1 & 104.7 \\ YOLOv8n & 82.6\% & 86.7\% & 89.3\% & 66\% & 13.9 & 87.3 \\ YOLOv10n & 81.2\% & 82.8\% & 87.4\% & 65.5\% & 10.7 & 69.9 \\ YOLOv11n & 81\% & 85.9\% & 88.6\% & 64.5\% & 10.6 & 69.6 \\ SugarRow-YOLO & 83\% & 87.8\% & 90.2\% & 69.2\% & 13.1 & 57.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons with other YOLO series models. To further evaluate the impact of different architectural modules on model complexity, Table 5 summarizes the changes in parameter count and GFLOPs when integrating various improvements into YOLOv11. It is worth noting that the UIoU loss function introduced during training does not contain any learnable parameters and incurs only minimal computational overhead during the training phase. As it has no impact on inference complexity or model size, it is not included in the table. As shown in the table, compared to the baseline YOLOv11, the incorporation of WTConv and C2 PSA_iRMB modules leads to a moderate increase in the number of layers (from 319 to 358), while the total number of parameters remains at a comparable level, ranging between approximately 2.48 million and 2.62 million.