# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 3 Experimental Validation and Results Analysis
### Comparative Experiments  
To demonstrate the accuracy of the improved model, comparative experiments were conducted using the same dataset, under identical conditions, and on the same device. In this experiment, metrics such as Precision (P), Recall (R), F1 score, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, GFLOPs, and others were utilized to evaluate the model's performance. Additionally, to validate the performance improvement of the modified model, comparative experiments were conducted with classic object detection networks such as YOLOv4-tiny, YOLOv7-tiny [42], YOLOv8s, YOLOx [43], TIA-YOLO [44], SSD [45], and Faster-RCNN, all within the same environment. The results of these experiments serve as indicators of the effectiveness of the CCCS-YOLO algorithm proposed in this paper. Table 3 presents the comparative experiments between CCCS-YOLO and other object detection algorithms.  
From Figure 7, the changes in the PR curve for YOLOv5 can be observed before and after improvement. In Figure 8, the training results indicate that the loss function did not consistently decrease to a point of minimal change during training. This suggests that the network did not experience overfitting. Additionally, the use of early stopping before training can prevent overfitting, ensuring the effectiveness of the training process.  
The comparative experiments across various algorithms in the table above reveal that CCCS-YOLO achieves the highest [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, and F1 scores when compared to other mainstream algorithms. In the comparison using the [EMAIL_ADDRESS]:0.95 parameter, our algorithm outperforms YOLOv4-tiny by 27.91%, Faster-RCNN by 22.5%, SSD by 27.4%, YOLOx by 4.9%, YOLOv5s by 5.1%, YOLOv7-tiny by 7.3%, and YOLOv8s by 4.6%. It attains a higher level of detection accuracy compared to other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**F1**} & **Precision** & **Weight** & **Params** & \multirow{2}{*}{**GFLOPs**} \\  & **(\%)** & **(\%)** & & & **(\%)** & **(MB)** & **(M)** & \\ \hline YOLOv4-tiny & 50.68 & 30.69 & 46.2 & 54.8 & 67.87 & 22.4 & 6.0 & 16.3 \\ Faster-RCNN & 53.46 & 36.1 & 60.1 & 52.8 & 47.1 & 108 & 41.13 & 78.1 \\ SSD & 46.48 & 31.2 & 56.9 & 65.0 & 75.8 & 91.1 & 50.4 & 114.2 \\ YOLOx & 74.84 & 53.7 & 71.4 & 74.4 & 77.69 & 34.3 & 8.94 & 26.64 \\ YOLOv5s & 76.3 & 53.5 & 71.5 & 74.1 & 76.9 & 13.7 & 7.1 & 15.8 \\ TIA-YOLOv5 & 75.63 & 52.4 & 72.4 & 74.8 & 77.5 & 16.8 & 9.4 & 17.6 \\ YOLOv7-tiny & 76.9 & 51.3 & 71.3 & 74.7 & 78.6 & 11.7 & 6.1 & 13.0 \\ YOLOv8s & 77.1 & 54.0 & 72.4 & 76.9 & 82.1 & 21.5 & 11.2 & 28.6 \\ CCCS-YOLO & 79.5 & 58.6 & 74.8 & 77.9 & 81.3 & 14.9 & 7.65 & 16.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison among different object detection algorithms.  
While CCCS-YOLO has slightly higher parameters and GFLOPs compared to YOLOv5s, it consistently outperforms the YOLOv5s model in various performance metrics. In conclusion, CCCS-YOLO, proposed in this paper, demonstrates superior detection performance while maintaining a relatively low parameter count and computational load, substantiating the excellence of our algorithm.