  Deep learning (DL) based object detection has achieved great progress. These
methods typically assume that large amount of labeled training data is
available, and training and test data are drawn from an identical distribution.
However, the two assumptions are not always hold in practice. Deep domain
adaptive object detection (DDAOD) has emerged as a new learning paradigm to
address the above mentioned challenges. This paper aims to review the
state-of-the-art progress on deep domain adaptive object detection approaches.
Firstly, we introduce briefly the basic concepts of deep domain adaptation.
Secondly, the deep domain adaptive detectors are classified into five
categories and detailed descriptions of representative methods in each category
are provided. Finally, insights for future research trend are presented.
