Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20\%, and to supervised models trained directly on the target data.

翻译：夜间目标检测因缺乏夜间图像标注而具有挑战性。尽管已有多种域自适应方法，但实现高精度结果仍存在问题。在采用成熟师生框架的方法中，尤其是针对小尺度及低光照目标，仍观察到假阳性错误传播现象。本文提出两阶段一致性无监督域自适应网络2PCNet以解决这些问题。该网络在第一阶段利用教师模型的高置信度边界框预测，将其附加到学生模型的区域提议中，供教师在第二阶段重新评估，从而生成高、低置信度伪标签的组合。夜间图像和伪标签在输入学生模型前进行尺度缩减，以提供更强的小尺度伪标签。为应对图像中低光照区域及其他夜间属性导致的错误，我们提出名为NightAug的夜间专用增强流水线，该流水线通过对日间图像应用随机增强（如眩光、模糊和噪声）来实现。在公开数据集上的实验表明，我们的方法相比现有最优方法提升20%，甚至优于直接在目标数据上训练的监督模型。