  In the field of object detection, domain generalisation (DG) aims to ensure
robust performance across diverse and unseen target domains by learning the
robust domain-invariant features corresponding to the objects of interest
across multiple source domains. While there are many approaches established for
performing DG for the task of classification, there has been a very little
focus on object detection. In this paper, we propose a domain penalisation (DP)
framework for the task of object detection, where the data is assumed to be
sampled from multiple source domains and tested on completely unseen test
domains. We assign penalisation weights to each domain, with the values updated
based on the detection networks performance on the respective source domains.
By prioritising the domains that needs more attention, our approach effectively
balances the training process. We evaluate our solution on the GWHD 2021
dataset, a component of the WiLDS benchmark and we compare against ERM and
GroupDRO as these are primarily loss function based. Our extensive experimental
results reveals that the proposed approach improves the accuracy by 0.3 percent
and 0.5 percent on validation and test out-of-distribution (OOD) sets,
respectively for FasterRCNN. We also compare the performance of our approach on
FCOS detector and show that our approach improves the baseline OOD performance
over the existing approaches by 1.3 percent and 1.4 percent on validation and
test sets, respectively. This study underscores the potential of performance
based domain penalisation in enhancing the generalisation ability of object
detection models across diverse environments.
