# Solar Filament Detection Based on an Improved Deep Learning Model
## 6 Conclusions  
In the field of filament detection, existing methods face issues such as unreliable dataset annotations and poor anti-interference capability. The work described in this article addresses the above issues. To address the problem of unreliable dataset annotations, this paper manually creates a new solar filament dataset with reduced mislabeling. To tackle the issues of interference, this paper proposes a hybrid CNN-Transformer detection model. Specifically, introducing Transformer can enhance the model's ability to counter interference. MRB is designed to address the wide range of solar filament sizes. Using deformable large kernel attention and a res path ensures the full integration of encoder and decoder information. Our proposed model can be used to detect solar filaments in full-disk H\(\alpha\) solar images observed by BBSO, outputting the result images with filament annotations.  
Compared to existing methods, the detection results of the proposed method show significant improvements in both quantitative and qualitative evaluations. In particular, the improvement is significant for images with sunspots and background interference.  
Although this method represents some progress, there are still some issues and limitations. When using this method, it is necessary to preprocess the observed images, which can increase the workload. Future work will include providing a convenient preprocessing program. Due to the introduction of Transformer and improvement modules, a large number of preset parameters, such as the number of Transformer layers and the size of the  
Figure 14: Another compilation of the detection results of different algorithms. (**a**) Full-disk H\(\alpha\) solar image; (**b**) Solar filament label map; (**c**) Detection result using improved U-Net; (**d**) Detection result using improved V-Net; (**e**) Detection result using our method; (**f-h**) Deviations between the results and annotations, obtained through the previously mentioned program. There’s a sunspot in the red box.  
Patch Embedding block, are introduced during network training. Future work will include conducting more detailed parameter design and ablation experiments to investigate the optimal parameter settings for achieving the best experimental results. The current dataset contains a limited number of images. Future work will focus on studying how to train an efficient detection model with a small number of images, as well as expanding the scale and quantity of the dataset.  
Z.S.: Conceptualization; formal analysis; funding acquisition; investigation; writing--review and editing. M.S.: Conceptualization; formal analysis; investigation; methodology; validation; writing--original draft; writing--review and editing. R.L.: Conceptualization; formal analysis; investigation; writing--review and editing. 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 number 12063002.  
The H\(\alpha\) image data used to support the findings of this study are observed by the Big Bear Solar Observatory. Image data files we used were downloaded from [[https://bbsoweb.bbso.njit.edu/pub/archive](https://bbsoweb.bbso.njit.edu/pub/archive)]([https://bbsoweb.bbso.njit.edu/pub/archive](https://bbsoweb.bbso.njit.edu/pub/archive)) (accessed on 25 December 2023). The training set and test set data of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.