# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 5 Experiments
### Experiments and Results Analysis
#### 5.3.1 Experiments on the Self-Constructed KRHO Dataset
For example, in the case of hazardous garbage, our approach achieves a precision, recall, and F1 score of 0.99--an improvement of 4-5 percentage points over the most competitive baseline--demonstrating accurate and stable classification of challenging samples. Moreover, in the high-frequency kitchen garbage category, our method maintains a perfect precision of 1.00, while its recall and F1 scores either exceed or are on par with the highest records observed for the alternative networks. This clearly underscores the method's robust capability in reliably detecting large volumes of everyday garbage. Similarly, for both other garbage and recyclable garbage, our method achieved precision, recall, and F1 scores close to 0.99, clearly demonstrating enhanced generalization and robustness in discriminating among diverse garbage types. In terms of aggregate metrics, whether considering overall accuracy or summary indicators such as macro averages and weighted averages, the results further confirm that our approach excels not only in major categories but also in class recognition under imbalanced sample distributions, thereby minimizing misclassifications and omissions. This highlights its robust learning capabilities and superior classification performance in complex scenarios. To quantitatively illustrate the accuracy and error rates of the five methods for garbage classification, we further conducted experiments using confusion matrices. The results are presented in Figure 11.  
As depicted in the confusion matrices, our proposed method exhibits a markedly stronger discriminative ability across the four garbage categories. Not only are the diagonal values significantly higher, but the misclassifications are also confined to only a few sparse locations, outperforming the other four networks considerably. Specifically, between easily confused categories such as hazardous garbage and recyclable garbage, alternative models tend to incur substantial cross-category misclassifications, whereas our method nearly eliminates these errors. In the case of kitchen garbage--a category with a large number of samples--our method also maintains an exceptionally low misclassification rate, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Categories/Indicators**} & \multicolumn{1}{c}{**DenseNet121**} & \multicolumn{1}{c}{**InceptionV3**} & \multicolumn{1}{c}{**MobileNetV2**} & \multicolumn{1}{c}{**ResNet50**} & \multicolumn{1}{c}{**Ours**} & \multicolumn{1}{c}{**Support**} \\ \hline \multirow{3}{*}{Hazardous garbage} & Precision & 0.95 & 0.95 & 0.83 & 0.89 & **0.99** & \\  & Recall & 0.94 & 0.94 & 0.81 & 0.88 & **0.99** & 178 \\  & F1 & 0.94 & 0.95 & 0.82 & 0.88 & **0.99** & \\ \hline \multirow{3}{*}{Kitchen garbage} & Precision & **1.00** & **1.00** & 0.98 & **1.00** & **1.00** & \\  & Recall & 0.99 & **1.00** & 0.97 & 0.99 & **1.00** & 690 \\  & F1 & 0.99 & **1.00** & 0.97 & 0.99 & **1.00** & 690 \\ \hline \multirow{3}{*}{Other garbage} & Precision & 0.94 & 0.94 & 0.81 & 0.91 & **0.98** & \\  & Recall & 0.92 & 0.93 & 0.72 & 0.81 & **0.98** & 262 \\  & F1 & 0.93 & 0.93 & 0.76 & 0.85 & **0.98** & \\ \hline \multirow{3}{*}{Recyclable garbage} & Precision & 0.96 & 0.97 & 0.88 & 0.93 & **0.99** & \\  & Recall & 0.98 & 0.98 & 0.93 & 0.98 & **0.99** & 682 \\  & F1 & 0.97 & 0.97 & 0.90 & 0.95 & **0.99** & \\ \hline Accuracy & - & 0.97 & 0.97 & 0.90 & 0.95 & **0.99** & 1812 \\ \hline \multirow{3}{*}{MA} & Precision & 0.96 & 0.96 & 0.88 & 0.93 & **0.99** & \\  & Recall & 0.96 & 0.96 & 0.86 & 0.91 & **0.99** & 1812 \\ \cline{1-1}  & F1 & 0.96 & 0.96 & 0.87 & 0.92 & **0.99** & \\ \hline \multirow{3}{*}{WA} & Precision & 0.97 & 0.97 & 0.90 & 0.95 & **0.99** & \\ \cline{1-1}  & Recall & 0.97 & 0.97 & 0.90 & 0.95 & **0.99** & 1812 \\ \cline{1-1}  & F1 & 0.97 & 0.97 & 0.90 & 0.95 & **0.99** & \\ \hline \hline \end{tabular} The best results are marked in bold.  
\end{table} 
Table 6: Evaluation report of the five models on the self-constructed KRHO dataset.