  Effective object detection in mobile robots is challenged by deployment in
diverse and unfamiliar environments. Online Source-Free Domain Adaptation
(O-SFDA) offers model adaptation using a stream of unlabeled data from a target
domain in online manner. However, not all captured frames contain information
that is beneficial for adaptation, particularly when there is a strong class
imbalance. This paper introduces a novel approach to enhance O-SFDA for
adaptive object detection in mobile robots via unsupervised data acquisition.
Our methodology prioritizes the most informative unlabeled frames for inclusion
in the online training process. Empirical evaluation on a real-world dataset
reveals that our method outperforms existing state-of-the-art O-SFDA
techniques, demonstrating the viability of unsupervised data acquisition for
improving adaptive object detection in mobile robots.
