# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 4 Experiments and Discussion
### Method Comparison  
#### 4.4.1 Quantitative Analysis  
This study compares the SWM with 13 existing models (Table 2). Since there are many models involved, the advantages of SWM are discussed in two types of comparisons. #1: Comparison between the SWM and other feature fusion-based models. The compared methods include UNet, NestedUnet, SwinTransformer, DAnet, etc., which achieve feature fusion through skip connections or attention mechanisms. Among them, SwinTransformer and NestedUnet perform better. Compared with the proposed SWM in this study, SwinTransformer lags behind in all four indicators by 2.7%, 1.3%, 3.2%, and 2.2%, with overall performance falling behind; NestedUnet performs 2.3% worse than this study's method in \(F1\) value, although its precision is slightly higher than this method, its recall rate is 5.8% lower, indicating a large number of missed detections, making it unsuitable for large-area monitoring and control. The SWM in this study allows bidirectional information transmission, enabling the high-dimensional feature space to obtain detailed information during the encoding process to compensate for information loss during feature extraction, while allowing the low-dimensional feature space to gain high-dimensional semantic information to obtain more comprehensive feature representation. #2: Comparison between the SWM and other models based on feature extraction enhancement. Comparison methods include PSPNet, Deeplabv3 plus, DDRNet, which adjust the convolution, pooling, and other operation strides to achieve a larger receptive field in the spatial domain, and DANet, BiSeNetv2, SegFormer, GCN, and other models that design some modules to improve their ability to extract contextual features. When comparing the SWM with the best-performing GCN model among these models, the SWM outperforms it by 1.2%, 0.5%, and 2.5% in terms of F1 value, precision, and recall rate, respectively. Although there is a decrease in precision of 0.2%, the SWM significantly reduces the number of missed detections. Overall, compared with the 13 existing methods, the main improvement of this study is a significant increase in recall rate, with the highest overall performance among the 14 methods, indicating that the spatial and contextual branch structures we designed are superior to the compared methods in feature extraction ability, and can better capture the semantic information of image targets, demonstrating good performance in solid-waste recognizing tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone + Head** & **Context** & **Spatial** & **Fusion** & **\(F1\)** & \(OA\)** & **\(Rec\)** & **\(Prec\)** \\ \hline A & \(\surd\) & & & & 85.791 & 92.972 & 83.915 & 87.752 \\ B & \(\surd\) & & \(\surd\) & & 87.869 & 93.956 & 86.574 & 89.203 \\ C & \(\surd\) & \(\surd\) & & & 88.142 & 94.123 & 86.392 & 89.964 \\ D & \(\surd\) & \(\surd\) & \(\surd\) & & 88.564 & 94.245 & 88.146 & 88.986 \\ E & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 88.875 & 94.390 & 88.626 & 89.125 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Ablation Study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **F1 (\%)** & \(OA\) & \(Rec\) & **Prec** \\ \hline PSPNet & 81.387 & 90.985 & 77.956 & 85.134 \\ DAnet & 83.076 & 91.632 & 81.23 & 85.008 \\ DDRNet & 83.246 & 91.546 & 83.068 & 83.424 \\ BiSeNetv2 & 84.482 & 92.413 & 81.683 & 87.479 \\ SegFormer & 85.113 & 92.719 & 82.318 & 88.104 \\ FCHarDNet & 85.189 & 92.713 & 82.889 & 87.62 \\ ConvNeXt & 85.286 & 92.738 & 83.239 & 87.436 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy comparison of methods.