# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 2 Related Work
### Unsupervised Domain Adaptation (UDA) for Object Detection  
To address the fundamental problem of domain shift, past work has primarily focused on exploring the transfer of rich knowledge from the source domain to the target domain, which helps reduce domain shift at the feature level [1, 10, 12, 18]. Common methods in this category include adversarial feature learning and semi-supervised Mean Teacher (MT) frameworks [37]. Adversarial feature learning aims to generate domain-invariant features through a min-max game to produce domain-invariant feature maps. [PERSON] et al. [1] first proposed adversarial training for image-level and instance-level features of multiple bounding boxes. Some methods enforce consistency regularization on instances of multiple categories. This approach has been applied to image classification. Our focus is on nighttime object detection, which is more complex as it involves low-light image recognition. The semi-supervised Mean Teacher framework [37] improves the performance of semi-supervised learning models by distilling pseudo-labels from unlabeled data [2, 4]. The quality of pseudo-labels is crucial. [PERSON] et al. [2] proposed a domain-adaptive object detection algorithm based on CycleGAN to help improve the quality of pseudo-labels. [PERSON] et al. [32] proposed a hierarchical feature alignment method, which effectively addresses domain adaptability issues in cross-domain object detection through step-by-step feature adaptation from global to local levels. Knowledge distillation techniques have been utilized to improve detection accuracy by transferring knowledge from a larger, well-trained teacher model to a smaller student model [38, 39, 40]. [PERSON] et al. [33] proposed a dual branch distillation method for cross-domain object detection, which is particularly effective for nighttime scenarios [33]. Another notable approach is the two-phase consistency training for day-to-night domain adaptation by [PERSON] et al. [41]. [PERSON] et al. [4] improved the generalization performance of object detection models in unknown target domains by increasing sample diversity between the source and target domains (Diversify) and enforcing distribution matching in feature space (Match) [42]. Research on nighttime object detection has also made significant progress. [PERSON] et al. [34] proposed a dynamic convolutional neural network for nighttime object detection, improving detection accuracy and robustness [7, 8, 16, 18]. [PERSON] et al. [35] used unsupervised image translation techniques to enhance object detection in nighttime traffic scenes [10, 14, 19, 43]. Moreover, low-light image enhancement techniques have played a key role in nighttime object detection [44, 45, 46, 47]. Some methods also combine adversarial feature learning to improve the quality of pseudo-labels. Although these methods have achieved promising results in most domain adaptation tasks,their adaptability is limited when domain shift is significant or when datasets are small. This is mainly because it is difficult to obtain high-quality pseudo-labels in extremely adverse environments [3; 6; 17]. In Unsupervised Domain Adaptation (UDA) methods, the goal of UDA is to learn transferable features so that the model can exhibit better adaptability and performance in tasks [1; 12]. Unlike traditional UDA methods that minimize the feature distribution discrepancy between the source and target domains, most cross-domain methods use \"labeled images\" in the target domain for supervised learning, as different domains often have entirely different characteristics. For example, CycleGAN translates source domain images into the target domain [20], and the style-translated labeled images are then used for supervised learning in the target domain. Other methods iteratively estimate pseudo-labels for unlabeled target images through clustering and learn the model in the target domain through supervised learning via a self-training scheme. Some methods use additional labels or external models for learning.