  Filaments are omnipresent features in the solar atmosphere. Their location,
properties and time evolution can provide information about changes in solar
activity and assist the operational space weather forecast. Therefore,
filaments have to be identified in full disk images and their properties
extracted from these images. Manual extraction is tedious and takes much time;
extraction with morphological image processing tools produces a large number of
false-positive detections. Automatic object detection, segmentation, and
extraction in a reliable manner allows to process more data in a shorter time.
The Chromospheric Telescope (ChroTel), Tenerife, Spain, the Global Oscillation
Network Group (GONG), and the Kanzelh\"ohe Observatory (KSO), Austria, provide
regular full-disk observations of the Sun in the core of the chromospheric
H-alpha absorption line. We present a deep learning method that provides
reliable extractions of filaments from H-alpha filtergrams. First, we train the
object detection algorithm YOLOv5 with labeled filament data of ChroTel. We use
the trained model to obtain bounding-boxes from the full GONG archive. In a
second step, we apply a semi-supervised training approach, where we use the
bounding boxes of filaments, to learn a pixel-wise classification of filaments
with u-net. Here, we make use of the increased data set size to avoid
overfitting of spurious artifacts from the generated training masks. Filaments
are predicted with an accuracy of 92%. With the resulting filament
segmentations, physical parameters such as the area or tilt angle can be easily
determined and studied. This we demonstrate in one example, where we determine
the rush-to-the pole for Solar Cycle 24 from the segmented GONG images. In a
last step, we apply the filament detection to H-alpha observations from KSO
which demonstrates the general applicability of our method to H-alpha
filtergrams.
