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Integrated Generative-Model Domain-Adaptation for Object Detection under Challenging Conditions

Realistic object detection scenarios, most notably the detection of objects such as pedestrians and traffic signs by autonomous vehicles, present a real challenge for state-of-the-art methods. Domain shifts further aggravate this problem when an autonomous vehicle has to operate and detect objects under more challenging conditions (e.g., rainy weather). In this paper, we demonstrate that the performance of challenging object detection scenarios can be improved rather significantly using a novel integrated Generative-model based unsupervised training and Domain Adaptation (GDA) framework. In particular, we exploit unsupervised image-to-image translation to generate annotated visuals that are representatives of a target challenging domain. Then, we use these generated annotated visuals in addition to unlabeled target domain data to train a domain adaptive object detection method. We show that using this integrated approach outperforms both methods, unsupervised image translation, and domain adaptation, when they are used separately. We evaluate the performance of the proposed GDA framework using real visuals captured by vehicles driving under rainy weather. Our simulation results show that GDA can achieve significant improvements in the detection of pedestrians and traffic signs when tested under real challenging conditions in comparison with state-of-the-art methods.