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

Xiangyu Shi, Yanyuan Qiao, Qi Wu, Lingqiao Liu, Feras Dayoub

Keywords: Domain Adaptation, Visual Perception, Object Detection

Abstract: Effective object detection in autonomous vehicles 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 an online manner. However, not all captured frames contain information beneficial for adaptation, especially in the presence of redundant data and class imbalance issues. This paper introduces a novel approach to enhance O-SFDA for adaptive object detection through 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 the adaptive object detector.

Submission Number: 3