# Solar Filament Detection Based on an Improved Deep Learning Model
## 5 Results and Discussion
### Results  
Comparison experiments are conducted with existing solar filament detection methods. The methods for comparison are as follows: 1. The improved U-Net-based filament detection method proposed in reference [20] (Improved U-Net). 2. The improved V-Net-based filament detection method proposed in reference [21] (Improved V-Net). All of these have released open-source code and are available for reproduction and comparison using the dataset built in this article. The experimental results of this comparison are summarized in Table 2.  
As shown in Table 2, compared to other methods mentioned above, this method has improved precision by 4.76 percent and 4.36 percent, reaching a score of 91.50%. Recall has increased by 1.40 percent and 0.06 percent, reaching 90.89%. The F1 score has also improved by 3.10 percent and 2.54 percent, reaching 91.19%. The quantitative experimental results demonstrate that this method exhibits an advancement in the field of solar filament detection compared to other methods. To more intuitively demonstrate the detection performance of this method, especially the results in cases of background noise interference and sunspot interference, we conducted case studies on the detection results of different methods under the same conditions and subjectively evaluated the detection cases. Figure 13 showcases a detection case in which the full-disk H\(\alpha\) image is cluttered with background noise interference, potentially leading to false detections by the network. As can be seen in Figure 13a, there are no filaments present in the red box. There is uneven illumination here, which can produce background noise interference. However, the improved U-Net and improved V-Net methods are overly sensitive and mistakenly detect the background noise as solar filaments, compromising the detection results. In contrast, the method proposed in this article effectively resists background noise interference, successfully avoiding the misdetection of background noise interference as solar filaments, thereby achieving a superior solar filament detection performance. The method described in this article uses a program to visualize the deviations between the results and annotations. First, connected regions in the images are detected. For each filament region, if it exists in both the result image and the annotation image, it is removed. Otherwise, it is retained. Figure 13f-h shows the results of the program. The method exhibits fewer deviation regions. Figure 14 presents another detection case, in which the main factor affecting the detection results is the interference from sunspots. The red box in the figure indicates a sunspot, which should not be detected as a solar filament. The improved U-Net and improved V-Net methods failed to accurately identify the characteri \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline Improved U-Net & 86.74\% & 89.49\% & 88.09\% \\ Improved V-Net & 87.14\% & 90.83\% & 88.65\% \\ This method & 91.50\% & 90.89\% & 91.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results. Figure 13: 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 this method; (**f–h**) Deviations between the results and annotations, obtained through the previously mentioned program. There is background noise in the red box. mistakenly detected it as a solar filament, compromising their detection performance. In contrast, our method can effectively recognize the characteristics of sunspots and avoid falsely detecting them as solar filaments, achieving better detection results. Figure 14f-h shows the deviations between the results and annotations, obtained through the previously mentioned program. The method exhibits fewer deviation regions.