  Object detection algorithms allow to enable many interesting applications
which can be implemented in different devices, such as smartphones and wearable
devices. In the context of a cultural site, implementing these algorithms in a
wearable device, such as a pair of smart glasses, allow to enable the use of
augmented reality (AR) to show extra information about the artworks and enrich
the visitors' experience during their tour. However, object detection
algorithms require to be trained on many well annotated examples to achieve
reasonable results. This brings a major limitation since the annotation process
requires human supervision which makes it expensive in terms of time and costs.
A possible solution to reduce these costs consist in exploiting tools to
automatically generate synthetic labeled images from a 3D model of the site.
However, models trained with synthetic data do not generalize on real images
acquired in the target scenario in which they are supposed to be used.
Furthermore, object detectors should be able to work with different wearable
devices or different mobile devices, which makes generalization even harder. In
this paper, we present a new dataset collected in a cultural site to study the
problem of domain adaptation for object detection in the presence of multiple
unlabeled target domains corresponding to different cameras and a labeled
source domain obtained considering synthetic images for training purposes. We
present a new domain adaptation method which outperforms current
state-of-the-art approaches combining the benefits of aligning the domains at
the feature and pixel level with a self-training process. We release the
dataset at the following link https://iplab.dmi.unict.it/OBJ-MDA/ and the code
of the proposed architecture at https://github.com/fpv-iplab/STMDA-RetinaNet.
