# Solar Filament Detection Based on an Improved Deep Learning Model
## 1 Introduction  
Solar filaments are composed of low-temperature, high-density plasma [1; 2]. They are closely related to space weather. Eruptions of solar filaments are extremely spectacular solar events that cause plasma ejection. The plasma ejected during eruptions can reach Earth within a few days, thus influencing Earth's magnetosphere. In severe cases, this can lead to disasters such as communication disruptions and navigation failures [3; 4; 5; 6]. In addition, solar filaments serve as carriers of magnetic free energy that is accumulated and stored within the corona, and they are one of the best tracers of magnetic flux ropes in the corona [7; 8; 9; 10]. Detection of solar filaments helps forecasters forecast space weather and explore the solar magnetic field. As solar observing equipment advances and the number of observed images grows, automatic detection of solar filaments becomes more important. Many methods have been proposed to process the large number of observed images automatically. Due to the dark color of solar filaments observed through the H\(\alpha\) (6563 A) spectral line, most existing methods use full-disk H\(\alpha\) images as input to detect solar filaments. The specific methods can be divided into three categories:  
1. Threshold-based segmentation methods. Researchers use threshold-based methods such as global thresholding, local thresholding, and adaptive thresholding to detect solar filaments [11; 12; 13; 14; 15]. Some methods also combine techniques such as mathematical morphology, region growing, and edge detection [16]. However, these methods are prone to misidentifying noise in the images as filaments, resulting in poor detectionresults. There are quite significant differences between the detection results and the actual results, which can easily mislead astronomers;
2. Machine learning-based methods. For instance, artificial neural networks are used to detect filaments [17; 18]. Such methods can efficiently extract features using appropriate feature extractors designed with prior knowledge. They also have fast model training speeds. However, feature extractors should be designed with rich prior knowledge during usage. Additionally, they have poor model generalization, and their detection results are susceptible to interference when facing more complex characteristics. The methods are not conducive to large-scale application, and the detection results still need to be improved;
3. Deep learning-based methods. Deep learning methods can better extract image characteristics and have achieved great success in image processing, attracting widespread attention from scholars [19; 20; 21]. Researchers have employed Convolutional Neural Networks (CNN) to investigate solar filament detection methods. Many methods have been proposed based on CNN models, such as Mask R-CNN [22], U-Net [23], V-Net [24], CondInst [25], and so on. Researchers have also used methods based on unsupervised and semi-unsupervised learning, but found the results were not as good as those based on CNN [26]. In contrast to machine learning methods, deep learning methods have higher generalization and robustness. They also have better detection results. However, these methods rely on accurate dataset annotations. Current methods suffer from the unreliability of annotations in existing datasets. Moreover, they have poor anti-interference capability. This often leads to issues such as incorrect detection of sunspots as solar filaments, loss of small filaments, and incorrect detection of background noise as solar filaments. Overall, among the existing solar filament detection methods, those based on deep learning exhibit the best detection performance and have become the most popular in the last three years. Therefore, this study uses a deep learning-based method to detect solar filaments. The goal of this method is to address the following two issues faced by existing methods: unreliable dataset annotations and interference from sunspots and background noise.