Deep learning-based object detection technology has recently made significant progress and has a wide range of applications in robotics, autonomous driving, traffic surveillance, etc. However, due to the distribution discrepancy between the training and testing datasets, the off-the-shelf detectors pre-trained using the data in a specific domain often show apparent performance degradation when applied in wild scenarios. To address this problem, a domain adaptation method for object detection in the frequency domain is proposed. In light of the energy concentration property of the discrete cosine transform, the proposed algorithm conducts domain adaptation for object detection by processing only a few of the most significant frequency coefficients, which reduces memory and computing resource consumption and alleviates the domain shift problem. The proposed method consists of two stages. In the first stage, it translates annotated training data from the source domain to the target domain using unsupervised image-to-image translation. Adversarial domain adaptation is then applied to the object detection model to align the features of the translated data and the real data in the target domain. The experimental results of the object detection under different weather conditions show that the proposed method ranks first among the four testing algorithms. Compared with the object detection model trained with only source domain data, it can increase the mAP value by 33.9%.