# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 3 Results and Discussion
### Multi-Method Comparative Experiment  
This section compares the proposed AD-MRCNN model with six existing frontal identification methods on the same dataset and experimental setup, employing multiple evaluation metrics to validate the performance of AD-MRCNN in frontal identification. The final results are presented in Table 3, from which the following conclusions can be drawn:  
Figure 5: The visualization results of frontal identification after adding adapters to the MRCNN network. (**a**) Results without using adapters in the MRCNN network; (**b**) results incorporating adapters into the MRCNN network. Among traditional numerical frontal analysis methods, as the number of diagnostic factors increases from one to two and the diagnostic functions become more complex, the scores across all evaluation metrics show continuous improvement. 2. Machine learning methods achieve significantly higher overall scores across multiple evaluation metrics compared to traditional numerical frontal analysis methods. This is because current machine learning approaches for automatic frontal identification take multiple meteorological elements as input, capturing the systematic and comprehensive characteristics of fronts by integrating diverse meteorological features. In contrast, traditional numerical methods rely on relatively singular meteorological elements, simpler functions, and face challenges in determining appropriate thresholds. The performance differences between traditional numerical methods and machine learning methods in meteorological front identification have been supported by research [23]. 3. Among machine learning methods, semantic segmentation-based approaches score lower than instance segmentation-based methods. This is primarily because instance segmentation networks, in addition to providing category and location information like semantic segmentation networks, also identify which specific frontal system each grid point belongs to, offering more detailed objective frontal information. The superior performance of the AD-MRCNN model is consistent with the studies [24; 32]. Next, in order to better evaluate the accuracy of each method in identifying frontal categories, we visualized the confusion matrices of four methods--CNN, FCN, Unet, and AD-MRCNN--on the test set for the identification of four frontal types (cold, warm, stationary, occluded), as shown in Figure 6. The analysis method of the confusion matrix is detailed in [33]. Since the total number of identified fronts in the test set is large, each row of the confusion matrix in Figure 6 is normalized by dividing the values by the total number of the corresponding frontal category and expressed as percentages, allowing for a more intuitive comparison of recognition performance across categories. The results demonstrate that the accuracy of the four methods in identifying each frontal category gradually improves with increasing network complexity and refinement. Among them, AD-MRCNN achieves the highest accuracy in classifying all four frontal types, with the fewest misclassifications into other frontal categories. Additionally, the number of fronts misclassified as non-frontal cases consistently decreases across the four methods, with AD-MRCNN exhibiting the lowest such errors. This indicates that the AD-MRCNN model outperforms other methods in accurately identifying various frontal types, thereby achieving the highest overall frontal recognition accuracy. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **ACC** & **P** & **R** & \(\mathbf{F_{1}}\) & \(\mathbf{mIoU}\) \\ \hline \(G_{\text{T}}\) & 65.6\% & 60.7\% & 57.3\% & 59.0\% & \(\times\) \\ TFP & 69.6\% & 64.5\% & 61.8\% & 63.1\% & \(\times\) \\ F-diagnose & 71.2\% & 65.2\% & 63.7\% & 64.5\% & \(\times\) \\ CNN [17] & 73.0\% & 67.8\% & 65.0\% & 66.4\% & 47\% \\ FCN & 75.1\% & 76.9\% & 74.5\% & 74.9\% & 56\% \\ U-Net [19] & 77.3\% & 83.4\% & 83.3\% & 83.3\% & 63\% \\ AD-MRCNN & 83.5\% & 85.2\% & 86.8\% & 85.1\% & 77\% \\ \hline \hline \end{tabular}
\end{table}