  To ensure reliable object detection in autonomous systems, the detector must
be able to adapt to changes in appearance caused by environmental factors such
as time of day, weather, and seasons. Continually adapting the detector to
incorporate these changes is a promising solution, but it can be
computationally costly. Our proposed approach is to selectively adapt the
detector only when necessary, using new data that does not have the same
distribution as the current training data. To this end, we investigate three
popular metrics for domain gap evaluation and find that there is a correlation
between the domain gap and detection accuracy. Therefore, we apply the domain
gap as a criterion to decide when to adapt the detector. Our experiments show
that our approach has the potential to improve the efficiency of the detector's
operation in real-world scenarios, where environmental conditions change in a
cyclical manner, without sacrificing the overall performance of the detector.
Our code is publicly available at https://github.com/dadung/DGE-CDA.
