Most object detection models for autonomous driving may experience a
significant drop in performance when deployed in real-world applications, due
to the well-known domain shift issue. Supervised object detection methods for
autonomous driving usually assume a consistent feature distribution between
training and testing data, however, such assumptions may not always be the case
when weather conditions differ significantly. For example, an object detection
model trained under clear weather may not perform well in foggy or rainy
weather, due to the domain gap. Overcoming detection bottlenecks in foggy or
rainy weather scenarios is a significant challenge for autonomous vehicles
deployed in the wild. To address the domain gap in different weather
conditions, This paper proposes a novel domain adaptive object detection
framework for autonomous driving in foggy and rainy weather. Our method
leverages both image-level and object-level adaptation to reduce the domain
discrepancy in image style and object appearance. Additionally, to enhance the
model's performance under challenging samples, we introduce a new adversarial
gradient reversal layer that performs adversarial mining on hard examples
alongside 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 demonstrate that
object detection performance is significantly improved when using our proposed
method in domain shift scenarios for autonomous driving applications.

这篇论文提出了一个新颖的领域自适应目标检测框架，用于自动驾驶在雾天和雨天。我们的方法利用图像级和目标级自适应来减少图像风格和目标外观的领域差异。此外，为了增强模型在具有挑战性样本下的性能，我们引入了一种新的对抗梯度翻转层，该层在领域自适应同时在困难样本上进行对抗挖掘。此外，我们提出通过数据增强生成辅助领域以强制实施新的领域级度量规则。公开基准测试的实验结果表明，在自动驾驶应用的领域转移场景中使用我们提出的方法显著提高了目标检测性能。