Low surface brightness (LSB) galaxies are galaxies with central surface
brightness fainter than the night sky. Due to the faint nature of LSB galaxies
and the comparable sky background, it is difficult to search LSB galaxies
automatically and efficiently from large sky survey. In this study, we
established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD),
which is a data-driven model for end-to-end detection of LSB galaxies from
Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on
deep learning are applied to the SDSS field images to identify LSB galaxies and
estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS
images, we detected 1197 LSB galaxy candidates, of which 1081 samples are
already known and 116 samples are newly found candidates. The B-band central
surface brightness of the candidates searched by the model ranges from 22 mag
arcsec −2 to 24 mag arcsec −2, quite consistent with the
surface brightness distribution of the standard sample. 96.46\% of LSB galaxy
candidates have an axis ratio (b/a) greater than 0.3, and 92.04\% of them
have fracDev_r\textless 0.4, which is also consistent with the standard
sample. The results show that the LSBG-AD model learns the features of LSB
galaxies of the training samples well, and can be used to search LSB galaxies
without using photometric parameters. Next, this method will be used to develop
efficient algorithms to detect LSB galaxies from massive images of the next
generation observatories.Comment: 11 pages, 9 figures,accepted to be published on MNRA