Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition

arXiv:2310.19258v2 Announce Type: replace
Abstract: 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 …

abstract acquisition arxiv cs.cv data deployment detection diverse domain domain adaptation environments free however improving information mobile model adaptation object robots type unsupervised