Object detection models trained on a source domain often exhibit significant performance degradation when deployed in unseen target domains, due to various kinds of variations, such as sensing conditions, environments and data distributions. Hence, regardless the recent breakthrough advances in deep learning-based detection technology, cross-domain object detection (CDOD) remains a critical research area. Moreover, the existing literature remains fragmented, lacking a unified perspective on the structural challenges underlying domain shift and the effectiveness of adaptation strategies. This survey provides a comprehensive and systematic analysis of CDOD. We start upon a problem formulation that highlights the multi-stage nature of object detection under domain shift. Then, we organize the existing methods through a conceptual taxonomy that categorizes approaches based on adaptation paradigms, modeling assumptions, and pipeline components. Furthermore, we analyze how domain shift propagates across detection stages and discuss why adaptation in object detection is inherently more complex than in classification. In addition, we review commonly used datasets, evaluation protocols, and benchmarking practices. Finally, we identify the key challenges and outline promising future research directions. Cohesively, this survey aims to provide a unified framework for understanding CDOD and to guide the development of more robust detection systems.

翻译：在源域上训练的目标检测模型，由于传感条件、环境以及数据分布等多种差异，在部署至未见过的目标域时，往往表现出显著的性能下降。因此，尽管基于深度学习的检测技术近期取得了突破性进展，跨域目标检测（CDOD）仍然是一个关键的研究领域。此外，现有文献仍较为零散，缺乏对域偏移背后结构性问题及自适应策略有效性的统一视角。本综述对CDOD进行了全面而系统的分析。我们从问题公式化入手，强调了域偏移下目标检测的多级本质。随后，基于自适应范式、建模假设和流程组件，我们通过一个概念性分类法对现有方法进行组织。更进一步，我们分析了域偏移如何在检测各阶段传播，并探讨了为何目标检测中的自适应本质上比分类更为复杂。此外，我们还综述了常用的数据集、评估协议和基准测试实践。最后，我们指出了关键挑战并勾勒出有前景的未来研究方向。整体而言，本综述旨在为理解CDOD提供一个统一框架，并指导更鲁棒检测系统的开发。