  Object detection is an essential technique for autonomous driving. The
performance of an object detector significantly degrades if the weather of the
training images is different from that of test images. Domain adaptation can be
used to address the domain shift problem so as to improve the robustness of an
object detector. However, most existing domain adaptation methods either handle
single target domain or require domain labels. We propose a novel unsupervised
domain classification method which can be used to generalize single-target
domain adaptation methods to multi-target domains, and design a
weather-invariant object detector training framework based on it. We conduct
the experiments on Cityscapes dataset and its synthetic variants, i.e. foggy,
rainy, and night. The experimental results show that the object detector
trained by our proposed method realizes robust object detection under different
weather conditions.
