  We present a data-driven approach to automatically detect L dwarfs from Sloan
Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based
on deep learning. The established L dwarf automatic detection (LDAD) model
distinguishes L dwarfs from other celestial objects and backgrounds in SDSS
field images by learning the features of 387 SDSS images containing L dwarfs.
Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in
the test set, we successfully detected 83 known L dwarfs with a recall rate of
89.25% for known L dwarfs. Several techniques are implemented in the LDAD model
to improve its detection performance for L dwarfs,including the deep residual
network and the feature pyramid network. As a result, the LDAD model
outperforms the model of the original Faster R-CNN, whose recall rate of known
L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect
L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting
in a recall rate of 94.42% for known L dwarfs. The newly identified candidates
include L dwarfs, late M and T dwarfs, which were estimated from color (i-z)
and spectral type relation. The contamination rates for the test candidates and
validation candidates are 8.60% and 9.27%, respectively. The detection results
indicate that our model is effective to search for L dwarfs from astronomical
images.
