Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data. In this paper, we propose a novel and effective four-step UDA approach that leverages self-supervision and trains source and target data concurrently. We harness self-supervised learning to mitigate the lack of ground truth in the target domain. Our method consists of the following steps: (1) identify the region with the highest-confidence set of detections in each target image, which serve as our pseudo-labels; (2) crop the identified region and generate a collection of its augmented versions; (3) combine these latter into a composite image; (4) adapt the network to the target domain using the composed image. Through extensive experiments under cross-camera, cross-weather, and synthetic-to-real scenarios, our approach achieves state-of-the-art performance, improving upon the nearest competitor by more than 2% in terms of mean Average Precision (mAP). The code is available at https://github.com/MohamedTEV/DACA.

翻译：无监督域适应（UDA）在将源域训练的检测器迁移至无标注数据的目标域时扮演关键角色。本文提出一种新颖且高效的四步UDA方法，该方法利用自监督学习同步训练源域与目标域数据。我们借助自监督学习缓解目标域缺乏真实标注的问题。该方法包含以下步骤：（1）识别每张目标图像中置信度最高的检测区域，将其作为伪标签；（2）裁剪该区域并生成其增强版本集合；（3）将这些增强版本组合成一张合成图像；（4）利用合成图像对网络进行目标域适配。通过在跨摄像头、跨天气条件及合成到真实场景下的广泛实验，该方法取得了业界领先性能，平均精度（mAP）相较现有最优方法提升超过2%。代码已开源至 https://github.com/MohamedTEV/DACA。