  Typically, object detection methods for autonomous driving that rely on
supervised learning make the assumption of a consistent feature distribution
between the training and testing data, this such assumption may fail in
different weather conditions. Due to the domain gap, a detection model trained
under clear weather may not perform well in foggy and rainy conditions.
Overcoming detection bottlenecks in foggy and rainy weather is a real challenge
for autonomous vehicles deployed in the wild. To bridge the domain gap and
improve the performance of object detection in foggy and rainy weather, this
paper presents a novel framework for domain-adaptive object detection. The
adaptations at both the image-level and object-level are intended to minimize
the differences in image style and object appearance between domains.
Furthermore, in order to improve the model's performance on challenging
examples, we introduce a novel adversarial gradient reversal layer that
conducts adversarial mining on difficult instances in addition to domain
adaptation. Additionally, we suggest generating an auxiliary domain through
data augmentation to enforce a new domain-level metric regularization.
Experimental findings on public V2V benchmark exhibit a substantial enhancement
in object detection specifically for foggy and rainy driving scenarios.
