  The presence of Galactic cirrus is an obstacle for studying both faint
objects in our Galaxy and low surface brightness extragalactic structures. With
the aim of studying individual cirrus filaments in SDSS Stripe 82 data, we
develop techniques based on machine learning and neural networks that allow one
to isolate filaments from foreground and background sources in the entirety of
Stripe 82 with a precision similar to that of the human expert. Our photometric
study of individual filaments indicates that only those brighter than 26 mag
arcsec$^{-2}$ in the SDSS $r$ band are likely to be identified in SDSS
Stripe~82 data by their distinctive colours in the optical bands. We also show
a significant impact of data processing (e.g. flat-fielding, masking of bright
stars, and sky subtraction) on colour estimation. Analysing the distribution of
filaments' colours with the help of mock simulations, we conclude that most
filaments have colours in the following ranges: $0.55 \leq g-r \leq 0.73$ and
$0.01 \leq r-i \leq 0.33$. Our work provides a useful framework for an analysis
of all types of low surface brightness features (cirri, tidal tails, stellar
streams, etc.) in existing and future deep optical surveys. For practical
purposes, we provide the catalogue of dust filaments.
