Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. Unfortunately, it has received much less attention than supervised object detection. Models that try to address this task tend to suffer from a shortage of annotated training samples. Moreover, existing methods of feature alignments are not sufficient to learn domain-invariant representations. To address these limitations, we propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training into a unified framework. An intermediate domain image generator is proposed to enhance feature alignments by domain-adversarial training with automatically generated soft domain labels. The synthetic intermediate domain images progressively bridge the domain divergence and augment the annotated source domain training data. A feature pyramid alignment is designed and the corresponding feature discriminator is used to align multi-scale convolutional features of different semantic levels. Last but not least, we introduce a region feature alignment and an instance discriminator to learn domaininvariant features for object proposals. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations. Further extensive experiments verify the effectiveness of each component and demonstrate that the proposed network can learn domain-invariant representations. Index Terms-Object Detection, Unsupervised Domain Adaptation I. INTRODUCTION O BJECT detection is a fundamental problem in computer vision, and has been extensively studied for decades. Over the past several years, deep learning has achieved remarkable success in this area [1], [2], [3]. To advance the state-of-the-art on large-scale benchmarks [4], [5], most deep learning based approaches require tremendous amount of annotated training data.In real-world applications, the manual annotating such large-scale data is time-consuming and laborintensive. Moreover, it is challenging to deploy a trained deep learning model to new environments, even if the task is the same. This is simply because the performance is negatively impacted by changes in conditions such as image sensors, viewpoints, weather and time of day.There is a non-negligible discrepancy between the distribution of data from the target domain and that from the source domain.