A Robust Deep Learning Framework for Prominence Detection through Composite Feature Representations

Abstract

Solar prominences are dynamic structures suspended within the solar corona and are manifestation of solar activity. Their evolution includes eruptions linked to coronal mass ejections, making their detection critical for space weather monitoring and forecasting. The vast amounts of high-cadence data provided by missions such as SDO/AIA motivate the application of deep learning frameworks capable of assimilating large-scale datasets. However, previous studies have reported poor model performance caused by contamination from hot coronal emission from the EUV HeII 304 {\AA} channel. Using an existing labeled prominence dataset, we find that trained YOLOv5 object detection models exhibit a strong bias towards the 304 {\AA} colormap, rather than physically meaningful prominence features. We develop a further two models comprising three-channel images constructed through an original dataset preprocessing pipeline: (i) full-disk grayscale, full-disk enhanced corona, and disk-removed, (ii) same as (i) with all disk-removed images. Our pipeline corrects instrument degradation to maintain more consistent feature representations across the solar cycle. The composite model (i) achieves a mAP@50 of 0.749 and a recall of $78\%$ on the test set, outperforming previous bounding box methods. Visual analysis of the composite models reveals that many apparent false positives are valid unlabeled prominences. We additionally demonstrate cross-instrument generalization by testing the composite model on SUVI image data. By examining dataset biases that propagate into model predictions, we provide recommendations for robust dataset construction. We present a reliable, physically-motivated, and versatile deep learning model to automatically detect prominences in EUV images, providing a framework beneficial for space weather applications.