A Progressive Domain Adaptation for Object Detection via Coarse-grained Foreground Guidance

Domain adaptive object detection has always been a problem widely concerned by academia and industry. The variability of application scenes and the unknown target labels in the target domain make it challenging for well-trained detectors to generalize well by supervised learning. Due to the lack of instance-level information, previous approaches often tend to achieve image-level and pixel-level alignment using the model's own knowledge, but this is of finite help in separating the object to be detected from the image background, and it is difficult for the model itself to learn the foreground characteristics of the target domain. In this paper, we propose a method for domain-adaptive detection based on coarse-grained foreground guidance. Our approach uses an unsupervised foreground extraction algorithm to estimate the coarse locations of objects in the video, which can provide knowledge for instance features and progressively improve the cross-domain detection ability of the model. Experiments on the cross-domain object detection dataset confirm the effectiveness of our approach. We have also conducted experiments in real outdoor traffic scenarios, and the results indicate that our method can operate in real application scenarios.