# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
###### Abstract  
Due to the lack of annotations for nighttime low-light images, object detection in low-light images has always been a challenging problem. Achieving high-precision results at night is also an issue. Additionally, we aim to use a single nighttime dataset to complete the knowledge distillation task while improving the detection accuracy of object detection models under nighttime low-light conditions and reducing the computational cost of the model, especially for small targets and objects contaminated by special nighttime lighting. This paper proposes a Nighttime Unsupervised Domain Adaptation Network (NUDN) based on knowledge distillation to address these issues. To improve the detection accuracy of nighttime images, high-confidence bounding box predictions from the teacher and region proposals from the student are first fused, allowing the teacher to perform better in subsequent training, thus generating a combination of high-confidence and low-confidence pseudo-labels. This combination of feature information is used to guide model training, enabling the model to extract feature information similar to that of source images in nighttime low-light images. Nighttime images and pseudo-labels undergo random size transformations before being used as input for the student, enhancing the model's generalization across different scales. To address the scarcity of nighttime datasets, we propose a nighttime-specific augmentation pipeline called LightImg. This pipeline enhances nighttime features, transforming them into daytime features and reducing issues such as backlighting, uneven illumination, and dim nighttime light, enabling cross-domain research using existing nighttime datasets. Our experimental results show that NUDN can significantly improve nighttime low-light object detection accuracy on the SHIFT and ExDark datasets. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. k +
Footnote †: journal: Medical  
difficulty of detection [11; 12]. Collecting sufficient nighttime data for manual annotation via cameras is also costly. The results of the LightImg image enhancement, compared to the unenhanced images, are shown in Figure 1. This has prompted researchers to focus on improving the model's adaptability across domains. Domain adaptation is an effective solution that allows us to use existing datasets. Unsupervised domain adaptation (UDA) methods attempt to adapt the model learned in the source domain to the target domain without annotated labels. Most existing cross-domain methods directly learn in the target domain by using labeled images from the source domain or pseudo-label estimates for unlabeled images from the target domain [11; 12; 13; 14]. These methods do improve the accuracy of models in the target domain [2; 5; 10]. However, the learned models still struggle to generalize to unseen domains and require a large number of unlabeled target domain images for adaptation [15; 16; 17; 18], which is clearly impractical in the context of day-to-night adaptation. In the source domain, a large amount of labeled image data is required, while in the target domain, the style or resolution of the images may differ. Adversarial learning is then used with image and instance-level classifiers. However, these methods isolate the domain adaptation task purely within the feature extractor, suppressing the features of the target data to maintain domain invariance. Recent unsupervised domain adaptation methods have utilized knowledge distillation. Since the student undergoes supervised learning during the pre-training phase, it is sensitive to data variations. To address this issue, adversarial learning methods are used. In day-to-night tasks, methods that make daytime image features more similar to nighttime features are also used. However, in day-to-night tasks, such methods can be contaminated by a large number of low-quality pseudo-labels generated by the teacher.