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, however 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 detectionin 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.

翻译：通常，依赖监督学习的自动驾驶目标检测方法假设训练数据和测试数据之间的特征分布保持一致，然而这一假设在不同天气条件下可能失效。由于域间隙的存在，在晴朗天气下训练的检测模型可能在雾雨天气下表现不佳。克服雾雨天气下的检测瓶颈是部署于实际环境的自动驾驶车辆面临的实际挑战。为弥合域间隙并提升雾雨天气下的目标检测性能，本文提出了一种新颖的域自适应目标检测框架。通过图像级和物体级自适应，旨在最小化域间图像风格和物体外观的差异。此外，为提升模型对困难样本的性能，我们引入了一种新颖的对抗梯度反转层，在域自适应之外对难例进行对抗挖掘。同时，我们建议通过数据增强生成辅助域，以施加新的域级度量正则化。在公开V2V基准上的实验结果表明，针对雾雨驾驶场景的目标检测性能得到了显著提升。