  Domain adaptive object detection (DAOD) aims to alleviate transfer
performance degradation caused by the cross-domain discrepancy. However, most
existing DAOD methods are dominated by outdated and computationally intensive
two-stage Faster R-CNN, which is not the first choice for industrial
applications. In this paper, we propose a novel semi-supervised domain adaptive
YOLO (SSDA-YOLO) based method to improve cross-domain detection performance by
integrating the compact one-stage stronger detector YOLOv5 with domain
adaptation. Specifically, we adapt the knowledge distillation framework with
the Mean Teacher model to assist the student model in obtaining instance-level
features of the unlabeled target domain. We also utilize the scene style
transfer to cross-generate pseudo images in different domains for remedying
image-level differences. In addition, an intuitive consistency loss is proposed
to further align cross-domain predictions. We evaluate SSDA-YOLO on public
benchmarks including PascalVOC, Clipart1k, Cityscapes, and Foggy Cityscapes.
Moreover, to verify its generalization, we conduct experiments on yawning
detection datasets collected from various real classrooms. The results show
considerable improvements of our method in these DAOD tasks, which reveals both
the effectiveness of proposed adaptive modules and the urgency of applying more
advanced detectors in DAOD. Our code is available on
\url{https://github.com/hnuzhy/SSDA-YOLO}.
