# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
###### Abstract
In our investigation, the problem is mainly due to the significant changes between day and night environments. These erroneous pseudo-labels are repeatedly used as benchmarks during training, leading to poor object detection performance. To address this problem, we propose the NUDN method, as shown in Figure 2, a nighttime unsupervised cross-domain object detection approach based on knowledge distillation. Our network is the first to use enhanced images based on nighttime environmental images for cross-domain training. Our NUDN first converts nighttime images to daytime images. Then, NUDN merges the bounding boxes of pseudo-labels produced by the teacher with the regions proposed by the student's Region Proposal Network (RPN). The teacher then uses the merged results to generate new pseudo-labels. These pseudo-labels are then matched with the student's predictions. The knowledge conveyed by Teacher RPN to  
Figure 1: Display the results of our rapid image enhancement module, LightImg. The left side shows the enhanced images, while the right side displays the results under low-light conditions. students is shown in Figure 2. We can then use a weighted approach to ensure that our network is based on high-confidence pseudo-labels [19; 20]. To enhance nighttime images, daytime images exhibit various features absent in nighttime scenes, such as rich colors, detailed lighting, and low noise levels. Since the student is trained on daytime images, nighttime images cannot be overly enhanced to prevent the network from overly favoring daytime features. At the same time, existing low-light image enhancement methods are based on Retinex theory [21], using either network or model-based approaches. However, both network-based and model-based methods require significant computation time, which is intolerable. To address this issue, we propose a nighttime enhancement method called LightImg (Figure 3). LightImg is designed to brighten images quickly, flexibly, and robustly in real low-light scenarios. As a fundamental module, it significantly reduces computational costs, enabling us to conduct cross-domain research using only nighttime datasets. To address the issue of misidentifying small and medium-sized objects at night, we have designed a technique that can randomly scale images across various sizes. This technique resizes images and pseudo-labels to random sizes, enabling the model to robustly detect objects of various sizes and preventing overfitting at any single scale. To avoid  
Figure 2: This is an overview of our proposed framework, NUDN. NUDN consists of two main components: a student network, which is trained on labeled daytime images and unlabeled nighttime images, and a teacher network, which utilizes the exponential moving average (EMA) of the student to provide pseudo-labels for the student. affecting the teacher's performance, the images used by the teacher retain their original size. This allows large objects to be disguised as smaller ones, thereby improving the student network's ability to detect small and medium-sized objects. Overall, we achieve qualitative improvements in our results. In summary, the contributions of this paper include:  
* We propose NUDN, a teacher-student learning method with co-learning capabilities. NUDN utilizes a combination of high-confidence teacher labels and low-confidence student proposals. This strategy significantly enhances the utilization of pseudo-labels. * To enable cross-domain research using existing nighttime datasets, we propose the image enhancement technique LightImg, which can convert nighttime features into daytime features. * The use of random image scaling mitigates data limitations. * We conducted comprehensive evaluations on two nighttime datasets, demonstrating that our NUDN exhibits strong discriminative capabilities in the source domain, robust adaptability to single-target domains, and strong generalization abilities to unseen domains.