# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Datasets
#### 4.4.4 LightImg Time and Performance Comparison  
To further compare the image enhancement capabilities of our LightImg method, we evaluated the performance of the LightImg component on the ExDark dataset. Figure 6 and Table 5 jointly present the performance of LightImg in low-light image enhancement. Compared to LIME and KinD++, LightImg not only excels in enhancing image brightness but also demonstrates significant advantages in maintaining natural colors and enhancing local contrast. LIME often leads to overexposure, while KinD++ is less effective at enhancing  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Structure** & **I** & **CL** & **LR** & **AP** & **APs** & **APm** & **API** \\ \hline \multirow{3}{*}{Single} & & & & 31.7 & 4.8 & 16.5 & 30.4 \\  & ✓ & & & 36.8 & 9.1 & 23.3 & 41.4 \\ \cline{1-1}  & ✓ & ✓ & & 36.2 & 9.4 & 24.7 & 42.9 \\ \cline{1-1} \cline{3-10}  & ✓ & ✓ & ✓ & 37.4 & 14.1 & 28.8 & 42.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation studies were conducted on the ExDark dataset to evaluate the contributions of different components of our method. The components tested include consistency learning (CL), LightImg (I), and Label Randomization (LR).  
Figure 5: The comparative study results of TDD, 2 PCNet, and our method on the SHIFT dataset show that TDD has a significant number of false positive boxes compared to our method. In contrast, our performance is very close to that of 2 PCNet.  
dark regions. LightImg, through precise illumination control, avoids these issues, resulting in images that are more balanced both overall and in detail after enhancement.  
The visual comparison results in Figure 6 further demonstrate the effectiveness of our proposed LightImg method in enhancing low-light images. Compared to the LIME and KinD++ methods, LightImg not only excels in brightness enhancement but also exhibits significant advantages in preserving natural colors and improving local contrast. Notably, the LIME method often results in overexposure, while KinD++ performs poorly in enhancing dark areas. In contrast, LightImg, through precise illumination control, effectively avoids these issues, ensuring that the enhanced images exhibit higher visual quality both overall and in detail.  
Table 5 provides further quantitative performance metrics. The PSNR and SSIM values demonstrate LightImg's superiority in maintaining high image quality, while the lower LPIPS values indicate that its output images are more natural in visual perception. Although the processing time is slightly higher than that of the SCI method, LightImg strikes an excellent balance between time and quality, with a processing time significantly lower than that of LIME, meeting both practical and performance requirements. These results indicate the applicability and efficiency of LightImg in real-world scenarios, particularly in situations requiring high-quality image enhancement.  
Although LightImg performs excellently in various low-light nighttime scenarios, its ability to generate effective features may be limited under extremely challenging conditions such as very low lighting or strong backlighting (Figure 7). Experimental results in these scenarios indicate a decline in image enhancement effectiveness, which may also impact the quality of pseudo-labels [52]. This analysis not only reveals potential limitations of the method but also provides important directions for future optimization [53; 54].  
Figure 6: Visual comparison of a typical low-light image using various methods was conducted. (**a**) represents the original input image. The results indicate that LIME [29] tends to produce globally overexposed images, as depicted in (**b**), whereas KinD++ [51] does not effectively enhance local dark regions, as seen in (**c**). In contrast, the proposed method generates satisfactorily enhanced images, significantly improving illumination, color, and local contrast, as illustrated in (**d**).