# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 4 Conclusions  
This study presents AD-MRCNN, a novel deep learning framework for automated atmospheric front detection, addressing two critical limitations in existing methods: (1) suboptimal feature compatibility from raw meteorological inputs and (2) lack of instance-level frontal identification. Our key findings and contributions are summarized as follows:  
1. Adapter Module Enhances Feature Fusion and Training Efficiency  
The intelligent adapter module dynamically weights and fuses multi-source meteorological data (temperature, wind fields, humidity), resolving feature conflicts through a normalization-weighting-fusion pipeline. Ablation experiments demonstrated that adding the adapter reduced training epochs by \(\sim\)10 (Figure 4), indicating faster convergence. Improved classification metrics by 3-5% across CNN, FCN, U-Net, and MRCNN (Table 2). Suppressed false alarms (Figure 5), particularly in regions with strong non-frontal signals (e.g., land-sea boundaries). 2. Instance Segmentation Outperforms Semantic Segmentation  
Unlike traditional semantic segmentation (U-Net, FCN), AD-MRCNN provides instance-level identification, distinguishing individual frontal systems while classifying frontal types (cold/warm/stationary/occluded). Comparative experiments (Table 3) showed an 85.1% F1-score, surpassing U-Net (83.3%) and traditional methods (TFP: 63.1%), a 31% reduction in false alarms compared to semantic segmentation approaches, and a higher precision in frontal categorization (Figure 6), with fewer misclassifications (e.g., cold vs. warm fronts). 3. Robust Performance Across Meteorological Conditions  
The adapter's adaptive weighting mechanism improved detection in challenging scenarios, such as weak thermal gradients (e.g., cold air mass movement over oceans) and strong non-frontal signals (e.g., coastal temperature contrasts). The Mask R-CNN backbone with ROI Align ensured precise localization, even for small-scale fronts. Figure 6: Comparison of confusion matrices for frontal identification results by four types of networks. 4. Future Directions  
Future work includes generalizing AD-MRCNN to other meteorological tasks, such as trough line detection and cyclone tracking; integrating temporal dynamics by incorporating time-series data for frontal evolution prediction; and testing the model in real-time forecasting systems with higher-resolution inputs. In summary, AD-MRCNN advances automated front detection by combining adaptive feature fusion with instance-aware segmentation, achieving higher accuracy, better spatial precision, and improved robustness compared to existing methods. The modular design ensures flexibility for broader applications in meteorological feature detection. Conceptualization, X.D. and X.P. ; methodology, X.D. ; software, X.D. and T.W. ; validation, X.D., L.Z., Y.X. and Y.Z. ; formal analysis, X.D. ; investigation, X.D. and Y.X. ; resources, L.Z. and T.W. ; data curation, X.D. and Y.Z. ; writing--original draft preparation, X.D. ; writing--review and editing, X.D. and X.P. ; visualization, X.D. and Y.X. ; supervision, L.Z. and Y.X. ; project administration, L.Z. and T.W. ; funding acquisition, X.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant Nos. U2242021, 40275139, 41305138, and 42105146), the Open Fund of Key Laboratory of Smart Earth, the China Postdoctoral Science Foundation (Grant No. 2017M621700), the Hunan Province Natural Science Foundation (Grant Nos. 2021 JC0009 and 2021 JJ30773), the Fengyun Application Pioneering Project (FY-APP 2022.0605), and the Key Laboratory of Heavy Rain and Drought Flood.