  Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that
are commonly observed in high-redshift (z>1) galaxies but their formation and
role in galaxy evolution remain unclear. High-resolution observations of
low-redshift clumpy galaxy analogues are rare and restricted to a limited set
of galaxies but the increasing availability of wide-field galaxy survey data
makes the detection of large clumpy galaxy samples increasingly feasible. Deep
Learning, and in particular CNNs, have been successfully applied to image
classification tasks in astrophysical data analysis. However, one application
of DL that remains relatively unexplored is that of automatically identifying
and localising specific objects or features in astrophysical imaging data. In
this paper we demonstrate the feasibility of using Deep learning-based object
detection models to localise GSFCs in astrophysical imaging data. We apply the
Faster R-CNN object detection framework (FRCNN) to identify GSFCs in low
redshift (z<0.3) galaxies. Unlike other studies, we train different FRCNN
models not on simulated images with known labels but on real observational data
that was collected by the Sloan Digital Sky Survey Legacy Survey and labelled
by volunteers from the citizen science project `Galaxy Zoo: Clump Scout'. The
FRCNN model relies on a CNN component as a `backbone' feature extractor. We
show that CNNs, that have been pre-trained for image classification using
astrophysical images, outperform those that have been pre-trained on
terrestrial images. In particular, we compare a domain-specific CNN -`Zoobot' -
with a generic classification backbone and find that Zoobot achieves higher
detection performance and also requires smaller training data sets to do so.
Our final model is capable of producing GSFC detections with a completeness and
purity of >=0.8 while only being trained on ~5,000 galaxy images.
