# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions
### Ablation Experiments  
To validate the effectiveness of GE-YOLO, we conducted ablation experiments. The results are shown in Table 7.  
Based on the results of the ablation experiments, it is evident that the model's mAP and F1 Score improve significantly with the incorporation of various enhancements. Starting with the YOLOv8 baseline model, we introduced Gold-YOLO to the Neck network. This modification resulted in improvements in both mAP and F1 Score, reaching 92.1%  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline RT-DETR & 25.9 & 16.7 & 12.1 & 20.35 & 103.4 & 31.99 & 14.6 \\ YOLOv3 & 91.8 & 86.5 & 91.4 & 89.13 & 282.8 & 103.667 & 17.2 \\ YOLOv4 & 79.4 & 79.9 & 83.9 & 79.65 & 119.7 & 52.5 & - \\ YOLOv5 & 91.1 & 86.7 & 92.2 & 88.85 & 7.1 & 2.5 & 140.3 \\ YOLOv6 & 92.8 & 84.8 & 91.0 & 88.71 & 11.8 & 4.23 & 170.3 \\ YOLOv7 & 86.7 & 87.1 & 89.8 & 86.9 & 105.1 & 37.2 & - \\ YOLOv8 & 92.5 & 84.8 & 91.2 & 88.49 & 8.1 & 3.01 & 187.6 \\ YOLOv9 & 96.0 & 85.5 & 91.9 & 90.45 & 102.3 & 25.32 & 30.9 \\ YOLOv10 & 90.6 & 82.5 & 90.3 & 86.64 & 98.7 & 20.46 & - \\ YOLOv11 & 94.1 & 85.2 & 91.7 & 89.4 & 6.4 & 2.59 & - \\
**GE-YOLO(Ours)** & **92.1** & **88.6** & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of different algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Strategy** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline None & 93.1 & 84.2 & 91.6 & 88.4 & 8.2 & 3.15 & 192.1 \\ BiFFN & 91.2 & 88.4 & 92.2 & 89.8 & 8.1 & 2.57 & 122.7 \\
**Gold-YOLO** & **92.1** & **88.6** & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Different Feature Fusion Strategies.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Strategy** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline LDConv & 88.3 & 86.1 & 90.5 & 87.2 & 17.1 & 7.82 & 28.6 \\ CG Block & 91.3 & 85.6 & 91.4 & 88.4 & 16.5 & 7.64 & 69.6 \\ RFAConv & 94.1 & 85.8 & 92.1 & 89.8 & 16.5 & 8.10 & 71.8 \\ RepNCSPELAN4 & 93.5 & 87.2 & 92.3 & 90.2 & 16.3 & 7.63 & 76.5 \\ ODConv & 88.4 & 85.1 & 91.1 & 86.7 & 16.7 & 9.23 & 72.7 \\
**None** & **92.1** & **88.6** & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of Different Backbone Network Improvement Strategies.  
and 89.3%, respectively, which represents an increase of 0.9% and 0.8% compared to the baseline model.  
Next, using GIOU loss function further enhanced the mAP and F1 Score by 0.1% each. Finally, The addition of the EMA attention mechanism contributed an additional 0.9% improvement in both mAP and F1 Score, reaching 93.1% and 90.3%, respectively. From the results of the ablation experiments, it can be observed that the introduction of the Gold-YOLO feature fusion and distribution mechanism, as well as the EMA attention mechanism, led to a decrease in FPS. Through analysis, we believe this is likely due to the increased number of model parameters resulting from the incorporation of the feature fusion and distribution mechanism and the EMA attention mechanism. These additional parameters require computation during the inference process, thereby prolonging the inference time and causing a drop in FPS. However, by accepting this computational cost, the model's detection metrics, such as mAP and F1 score, have been significantly improved. Moreover, GE-YOLO ultimately achieves 85.9 FPS, far exceeding the real-time requirement of 25 FPS. Therefore, GE-YOLO is capable of handling real-time detection tasks while maintaining high detection accuracy.