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

Source: [Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition](<https://www.sota2.com/research/paper/68fa74a11e209131cea62c59>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:14:35.342Z

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

Published: 2023-10-30T04:04:02.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.19258>)

[PDF](<https://arxiv.org/pdf/2310.19258.pdf>)