# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 3 Results
### Comparative Experiment  
To comprehensively evaluate the performance of the model proposed in this paper, the current mainstream single-stage and two-stage detection models will be selected and compared horizontally in the dados_tcc dataset. The comparison results are shown in Table 3. Two-stage models are selected as Faster-RCNN [23], DINO [49], RT-DETR [34] and Deformable-DETR [37]. For single-stage models, the most popular YOLO series models and EfficientNet [50] are selected, including YOLOv3t [51], YOLOv5m [35], YOLOv6m [36], YOLOv9m [46] and YOLOv10m [52].  
Based on the data analysis in Table 3, our model demonstrates outstanding overall performance. Although compared with YOLOv9m [52], the mAP50 and mAP50:95 of our model decreased by 0.2% and 0.3%, respectively. However, the computational efficiency of our model has significantly improved. For example, GFLOPs have been reduced by 31%, and FPS far exceeds YOLOv9m [46]. Compared with YOLOv5m [35], although our model is 0.6% and 0.5% lower in Recall and mAP50:95, respectively, our model's FPS is 11 frames higher, and the number of parameters is reduced by 28%. In comparison with YOLOv3t [51], although YOLOv3t has a high FPS of 214 and low GFLOPs, its mAP50 and mAP50:95  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Value** & **Type** & **Value** \\ \hline Epoch & 200 & optimizer & AdamW \\ Batch size & 16 & learning rate & \(1\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model training parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP50 (\%)** & **mAP50:95 (\%)** & **GFLOPs (G)** & **Paras (M)** & **FPS** \\ \hline YOLOv3t & 84.6 & 73.8 & 81.4 & 61.0 & 18.9 & 12.1 & 214.1 \\ YOLOv5m & 91.6 & 82.4 & 88.2 & 78.3 & 64 & 25.0 & 39.3 \\ YOLOv6m & 87.7 & 81.0 & 86.9 & 77.3 & 161.1 & 52.0 & 46.3 \\ YOLOv9m & 92.6 & 80.0 & 88.6 & 78.1 & 76.5 & 20.0 & 38.3 \\ YOLOv10m & 92.0 & 75.6 & 85.3 & 77.1 & 63.4 & 16.5 & 40.0 \\ EfficientNet & 90.1 & 80.3 & 84.8 & 74.1 & 87.9 & 18.38 & 21.8 \\ RT-DETR & 87.8 & 78.4 & 85.2 & 75.6 & 56.9 & 19.9 & 45.6 \\ Faster-RCNN & 91.8 & 78.4 & 88.2 & 72.6 & \(-\) & 41.4 & 7.3 \\ DINO & 91.6 & 76.1 & 83.5 & 74.2 & \(-\) & 47.5 & 4.3 \\ Deformable-DETR & 89.2 & 68.8 & 87.4 & 71.8 & \(-\) & 40.1 & 5.8 \\ Ours & 92.7 & 81.8 & 88.4 & 77.8 & 52.5 & 18.0 & 50.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results.  
are 7% and 16.8% lower than those of our model, respectively. This makes it difficult to achieve accurate recognition in the weed detection task, thus affecting the actual application effect. Other single-stage detection models also marginally underperformed compared to our proposed approach. Our model achieved 88.4% and 77.8% on the mAP 50 and mAP 50:95 index on the dados_tcc dataset, while FPS also achieved a good performance of 50.3. However, the two-stage detection model is much lower than the proposed model in average accuracy and FPS. In conclusion, our model maintains robust competitive performance in detection capabilities compared to these mainstream detection models.