  Filament identification became a key step to tackling fundamental problems in
various fields of Astronomy. Nevertheless, existing filament identification
algorithms are critically user-dependent and require individual
parametrization. In this study, we aimed at adapting the neural networks
approach to elaborate the best model for filament identification that would not
require fine-tuning for a given astronomical map. First, we created training
samples based on the most commonly used maps of the interstellar medium
obtained by Planck and Herschel space telescopes and the atomic hydrogen
all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used
algorithm for filament identification, to produce training outputs. In the next
step, we trained different neural network models and discovered that a
combination of the Mask R-CNN and U-Net architecture is most appropriate for
filament identification and determination of their orientation angles. We
showed that neural network training might be performed efficiently on a
relatively small training sample of only around 100 maps. Our approach
eliminates the parametrization bias and facilitates filament identification and
angle determination on large data sets.
