Unsupervised domain adaptation for object detection through mixed-domain and co-training learning

As the data distribution difference between the target domain (test sample set) and the source domain (training sample set) increases, it may lead to a sharp decline in the performance of the object detection network. However, it is expensive or impossible to obtain massive labeled data directly in the target domain.Therefore, domain adaptation techniques are needed to solve this problem. Unsupervised domain adaptation can learn the domain invariant features of the source domain and the target domain, thereby ensuring the performance of object detection. In this paper, we propose a novel multi-step training approach to accomplish the task of domain-adaptive object detection, using a hybrid domain training and co-training solution. 1) Hybrid domain training uses the source domain and the target domain to generate an intermediate domain, and then mixes the source domain and the intermediate domain into a hybrid domain to participate in training, making full use of domain features. 2) Co-training combines the predictions of the two branches with the same structure, but uses a synergistic loss function to force the two branches to observe features from different perspectives, labeling the target domain with higher quality pseudo-labels. We evaluate the proposed method and perform ablation experiments on datasets Citycape, Foggy Cityscape and SIM10K et al. The results show that our method can obtain more efficient results, and it is robust.