# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions  
### Comparative Analysis of the Improvement Strategies  
We propose a rice field weed detection network, GE-YOLO, which introduces the Gold-YOLO feature aggregation and distribution network, EMA attention mechanism, and GIoU loss function based on the YOLOv8 network structure. To rigorously evaluate the effectiveness of these improvements, we conducted a series of comparative experiments, testing different attention mechanisms, loss functions, and feature fusion strategies. Specifically, we compared five different attention mechanisms: EMA, AcMix [28], CBAM [29], ECA [30], and GAM [31]. These attention mechanisms were incorporated into the GE-YOLO network at the same position as the EMA attention mechanism, serving as replacements for the EMA attention mechanism. The performance metrics obtained were then compared, and the experimental results are presented in Table 2.  
From the experimental results, it can be observed that, for both mAP and F1 score metrics, the EMA attention mechanism achieved the best performance among all the attention mechanisms. In terms of the number of parameters and computational complexity, the network with the EMA attention mechanism also had the lowest values. Regarding inference speed, the network with the EMA attention mechanism achieved 85.9 FPS, only slightly lower than the network with the ECA attention mechanism. After considering the trade-off between efficiency and accuracy, we conclude that the EMA attention mechanism is the optimal choice. We also tested five different loss functions (EIoU [32], SIoU [33], WIoU [34], and CIoU, GIoU) on the GE-YOLO network to compare the performance metrics. The experimental results are presented in Table 3.  
From the experimental results, it can be seen that the network using the GIoU loss function achieved the best performance in both mAP and F1 scores. Although the inference speed (FPS) was slightly lower than that of the EIoU and SIoU networks, the GIoU loss function led to a significant improvement in weed detection accuracy. After considering the trade-off, we conclude that it is the optimal choice. To address the low resolution of small objects during multi-scale feature fusion, we also tested two different feature fusion strategies: the Gold-YOLO feature aggregation and distribution network and the bidirectional feature pyramid network (BiFPN) [35]. In this process, the GIoU loss function was kept constant, and the EMA attention mechanism was added at similar positions. The relevant performance metrics obtained from the comparative experiments are presented in Table 4.  
Based on the results of the comparative experiments, we found that the Gold-YOLO network leads to a significant increase in network parameters and a reduction in inference speed. However, Gold-YOLO achieves relatively high weed detection accuracy, and the FPS can still reach 85.9. After weighing the trade-offs, we chose Gold-YOLO as the improvement strategy for the original feature fusion network of YOLOv8. The use of BiFPN is also worth further investigation. Additionally, the backbone network of YOLOv8 may generate lower-resolution feature maps when handling small objects. Therefore, we further explored the possibility of improving the backbone network. We tested five different improvement strategies to enhance the conventional convolution operations in the YOLOv8 backbone, including LDConv [36], CG Block [37], RFAConv [38], RepNCSPELAN4 [18], and ODConv [39]. The experimental results are presented in Table 5.  
From these data, it can be concluded that although these modules theoretically improve the feature extraction performance of the backbone network, the actual test metrics (such as mAP and F1 scores) do not perform as well as the original GE-YOLO. Additionally, in terms of FPS, GE-YOLO also achieved the highest value. Therefore, we decided to retain the original structure of the backbone network and continue using the native YOLOv8 backbone network.