# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 5 Conclusions  
This paper proposes a network, NUDN, capable of performing cross-domain training using only existing low-light images. Specifically, during the pre-training phase, NUDN employs LightImg to enhance night-time images into daytime images, which are then fed to the teacher network for learning. In the formal learning phase, pseudo-labels generated by the teacher network are ranked by confidence level and assigned to the student network for learning. Additionally, we designed a Label Randomization technique to guide the model in identifying target locations across various images. NUDN significantly reduces the workload associated with existing day-night cross-domain tasks, demonstrating its effectiveness and applicability.  
Through the analysis of Figures 4-6, it is clear that the proposed NUDN method exhibits exceptional adaptability and performance across various nighttime scenes and low-light conditions. This success is not only attributed to the outstanding performance of the LightImg module in image enhancement but also to the synergistic combination of consistency learning and label randomization strategies, allowing the model to sustain high detection accuracy and robustness in diverse scenarios. In summary, these experimental results validate the effectiveness of our method and its potential for practical applications.  
Extensive quantitative and qualitative experiments demonstrate that our proposed method effectively addresses the issue of sparse datasets in cross-domain day-night recognition, significantly outperforming existing methods. In future work, we will explore research on day-night object re-identification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**ExDark**} & \multirow{2}{*}{**Times (ms)**} \\ \cline{2-2} \cline{4-5}  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** & \\ \hline KinD++ [33] & **13.44** & 0.570 & 0.205 & 281.4 \\ LIME [41] & 11.99 & **0.593** & **0.193** & 828.6 \\ SCI [47] & 10.25 & 0.470 & 0.306 & **8.7** \\ LightImg (Ours) & 11.36 & 0.522 & 0.258 & 42.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparison was conducted between the current state-of-the-art low-light image enhancement methods. The best results are in bold. The second best results are underlined. Each method has its own advantages.  
Figure 7: The performance of LightImg and NUDN under extremely low lighting conditions was assessed. It can be seen that in such conditions, images processed by LightImg exhibit significant noise. NUDN, under these extremely low-light conditions, is particularly sensitive to light source areas.  
**Author Contributions:** Conceptualization, C.Z. and D.L.; methodology, C.Z.; software, C.Z.; validation, C.Z. and D.L.; formal analysis, C.Z.; investigation, D.L.; resources, C.Z.; data curation, C.Z. and D.L.; writing--original draft preparation, C.Z. and D.L.; writing--review and editing, C.Z. and D.L.; visualization, C.Z. and D.L.; supervision, C.Z. and D.L.; project administration, C.Z. and D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Bisa Research Grant of Keimpung University in 2023 (No. 20230185).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.