Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision, we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://github.com/ZPDu/DAI-Net.

翻译：低光照场景下的目标检测是一个长期存在的挑战，由于能见度低，在良好光照数据上训练的检测器在低光照数据上表现出显著的性能下降。以往的方法通过使用真实低光照图像数据集探索图像增强或目标检测技术来缓解这一问题。然而，收集和标注低光照图像的固有困难阻碍了进展。为应对这一挑战，我们提出通过零样本昼夜域自适应来提升低光照目标检测性能，旨在使检测器从良好光照场景泛化到低光照场景，无需真实低光照数据。回顾低级视觉中的Retinex理论，我们首先设计了一个反射率表示学习模块，通过精心设计的照明不变性增强策略，学习图像中基于Retinex的照明不变性。接着，引入互换-重分解-一致性流程，通过执行两次连续图像分解并引入重分解一致性损失，改进了原始Retinex图像分解过程。在ExDark、DARK FACE和CODaN数据集上的大量实验表明，我们的方法具有强大的低光照泛化能力。代码可在 https://github.com/ZPDu/DAI-Net 获取。