Robust Object Detection via Adversarial Novel Style Exploration

Deep object detection models trained on clean images may not generalize well on degraded images due to the well-known domain shift issue. This hinders their application in real-life scenarios such as video surveillance and autonomous driving. Though domain adaptation methods can adapt the detection model from a labeled source domain to an unlabeled target domain, they struggle in dealing with open and compound degradation types. In this paper, we attempt to address this problem in the context of object detection by proposing a robust object Detector via Adversarial Novel Style Exploration (DANSE). Technically, DANSE first disentangles images into domain-irrelevant content representation and domain-specific style representation under an adversarial learning framework. Then, it explores the style space to discover diverse novel degradation styles that are complementary to those of the target domain images by leveraging a novelty regularizer and a diversity regularizer. The clean source domain images are transferred into these discovered styles by using a content-preserving regularizer to ensure realism. These transferred source domain images are combined with the target domain images and used to train a robust degradation-agnostic object detection model via adversarial domain adaptation. Experiments on both synthetic and real benchmark scenarios confirm the superiority of DANSE over state-of-the-art methods.