  Most object detection methods for autonomous driving usually assume a
consistent feature distribution between training and testing data, which is not
always the case when weathers differ significantly. The object detection model
trained under clear weather might not be effective enough in foggy weather
because of the domain gap. This paper proposes a novel domain adaptive object
detection framework for autonomous driving under foggy weather. Our method
leverages both image-level and object-level adaptation to diminish the domain
discrepancy in image style and object appearance. To further enhance the
model's capabilities under challenging samples, we also come up with a new
adversarial gradient reversal layer to perform adversarial mining for the hard
examples together with domain adaptation. Moreover, we propose to generate an
auxiliary domain by data augmentation to enforce a new domain-level metric
regularization. Experimental results on public benchmarks show the
effectiveness and accuracy of the proposed method. The code is available at
https://github.com/jinlong17/DA-Detect.
