  Low-surface-brightness galaxies (LSBGs), fainter members of the galaxy
population, are thought to be numerous. However, due to their low surface
brightness, the search for a wide-area sample of LSBGs is difficult, which in
turn limits our ability to fully understand the formation and evolution of
galaxies as well as galaxy relationships. Edge-on LSBGs, due to their unique
orientation, offer an excellent opportunity to study galaxy structure and
galaxy components. In this work, we utilize the You Only Look Once object
detection algorithm to construct an edge-on LSBG detection model by training on
281 edge-on LSBGs in Sloan Digital Sky Survey (SDSS) $gri$-band composite
images. This model achieved a recall of 94.64% and a purity of 95.38% on the
test set. We searched across 938,046 $gri$-band images from SDSS Data Release
16 and found 52,293 candidate LSBGs. To enhance the purity of the candidate
LSBGs and reduce contamination, we employed the Deep Support Vector Data
Description algorithm to identify anomalies within the candidate samples.
Ultimately, we compiled a catalog containing 40,759 edge-on LSBG candidates.
This sample has similar characteristics to the training data set, mainly
composed of blue edge-on LSBG candidates. The catalog is available online at
https://github.com/worldoutside/Edge-on_LSBG.
