  This paper focuses on source-free domain adaptation for object detection in
computer vision. This task is challenging and of great practical interest, due
to the cost of obtaining annotated data sets for every new domain. Recent
research has proposed various solutions for Source-Free Object Detection
(SFOD), most being variations of teacher-student architectures with diverse
feature alignment, regularization and pseudo-label selection strategies. Our
work investigates simpler approaches and their performance compared to more
complex SFOD methods in several adaptation scenarios. We highlight the
importance of batch normalization layers in the detector backbone, and show
that adapting only the batch statistics is a strong baseline for SFOD. We
propose a simple extension of a Mean Teacher with strong-weak augmentation in
the source-free setting, Source-Free Unbiased Teacher (SF-UT), and show that it
actually outperforms most of the previous SFOD methods. Additionally, we
showcase that an even simpler strategy consisting in training on a fixed set of
pseudo-labels can achieve similar performance to the more complex
teacher-student mutual learning, while being computationally efficient and
mitigating the major issue of teacher-student collapse. We conduct experiments
on several adaptation tasks using benchmark driving datasets including
(Foggy)Cityscapes, Sim10k and KITTI, and achieve a notable improvement of 4.7\%
AP50 on Cityscapes$\rightarrow$Foggy-Cityscapes compared with the latest
state-of-the-art in SFOD. Source code is available at
https://github.com/EPFL-IMOS/simple-SFOD.
