Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$\alpha$ Images

TL;DR

This paper introduces Flat U-Net, an ultralightweight and efficient model for solar filament segmentation in full-disk H-alpha images, achieving high accuracy with significantly reduced parameters for real-time solar observation applications.

Contribution

The paper presents a novel Flat U-Net architecture incorporating simplified channel attention and channel self-attention blocks, significantly reducing model size while improving segmentation performance over classical U-Net.

Findings

Achieved ~0.93 precision with pure SCA blocks.

Improved DSC and recall to 0.82 and 0.74 with CSA blocks.

Model outperforms classical U-Net in accuracy and efficiency.

Abstract

Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. Real-time automated identification of solar filaments is the most effective approach to managing large volumes of data. Existing models of filament identification are characterized by large parameter sizes and high computational costs, which limit their future applications in highly integrated and intelligent ground-based and space-borne observation devices. Consequently, the design of more lightweight models will facilitate the advancement of intelligent observation equipment. In this study, we introduce Flat U-Net, a novel and highly efficient ultralightweight model that incorporates simplified channel attention (SCA) and channel self-attention (CSA) convolutional blocks for the segmentation of solar filaments in full-disk H$\alpha$ images. Feature information from each network layer is fully extracted to reconstruct interchannel feature representations. Each block effectively optimizes the channel features from the previous layer, significantly reducing parameters. The network architecture presents an elegant flattening, improving its efficiency, and simplifying the overall design. Experimental validation demonstrates that a model composed of pure SCAs achieves a precision of approximately 0.93, with dice similarity coefficient (DSC) and recall rates of 0.76 and 0.64, respectively, significantly outperforming the classical U-Net. Introducing a certain number of CSA blocks improves the DSC and recall rates to 0.82 and 0.74, respectively, which demonstrates a pronounced advantage, particularly concerning model weight size and detection effectiveness. The data set, models, and code are available as open-source resources.