## 3 Experimental Results
### Evaluation of Detection Performance under Varying Noises  
As shown by the aforementioned experimental results, the performance of the proposed model is slightly better than the existing models in various low-light environments. Owing to its robustness against external factors such as light reflection, shadows, and darker conditions, LiDAR is far more reliable in low-light environments than cameras are. However, as LiDAR data are generated in the form of point clouds, the model may be susceptible to adverse weather conditions such as snow, rain, and fog. Therefore, to evaluate the proposed model's coping ability when LiDAR is exposed to external environmental noise, additional experiments were carried out by adding Gaussian noise, a common type of noise encountered in real-world conditions, to the original images. To isolate the impact of low-light conditions on detection performance, we conducted experiments by varying the Gaussian noise variance in an environment with 40% ambient brightness. The pedestrian detection performance of our proposed model was compared with those of similar models. The results are summarized in Table 2. Our findings indicate that the object detection model relying solely on depth maps exhibited a substantial decline in detection performance as the noise levels increased, with its accuracy reaching as low as 50%. By integrating this model with camera data, this performance degradation was mitigated. Similarly, models that incorporated additional preprocessing on depth maps suffered from performance degradation due to increased noise. However, a camera mitigated this issue, ensuring that the detection performance remained at a reasonable 60-70% level. Compared with other models, our proposed model exhibited superior robustness to noise, maintaining approximately 80% of its performance even under increased noise. Existing models are de  
Figure 6: Comparison of pedestrian detection performance of the proposed model and similar models at 40% brightness level: (**a**) depth map; (**b**) RGB + depth map; (**c**) [PERSON] and [PERSON] [12]; (**d**) [PERSON] [13]; (**e**) [PERSON] [14]; (**f**) proposed model. ficient in noise effect suppression and solely focus on the contour information of all objects. Alternatively, our proposed model, which smoothens the scale of LiDAR by considering it to be noise and further overlaying the contour information of potentially occluded objects, demonstrates the most superior performance. Figure 7 shows the object detection results based on an image generated by assuming that Gaussian noise with a variance of 0.5% can be introduced through LiDAR. Figure 7(a) shows that in the case of detecting objects using only the depth map, most objects cannot be detected, and it completely fails to function as a detection model. In contrast, Figure 7(b)-f show a considerable decrease in the number of undetected objects by fusing RGB images and applying preprocessing to the depth map. In particular, the proposed model had a higher detection performance than other models and detected all objects. These results indicate that the proposed model exhibits strong noise robustness, demonstrating its reliability in various low-light night-time scenarios and noisy environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Filter Used for Depth** \\ **Map Preprocessing** \\ \end{tabular} } & \multicolumn{4}{c}{**Noise Level**} \\ \cline{3-5}  & & **0\%** & **0.2\%** & **0.5\%** \\ \hline Depth Map & - & 83.49 & 69.84 & 51.26 \\ Depth Map + RGB & - & 85.60 & 80.34 & 75.37 \\ [PERSON] and [PERSON] [12] & Morphology dilation & 86.05 & 74.88 & 65.55 \\ [PERSON] [13] & Unsharp Mask & 85.76 & 77.87 & 68.75 \\ [PERSON] and [PERSON] [14] & Canny Edge & 85.08 & 81.96 & 77.62 \\ Proposed model & Gaussian–Sobel & 87.03 & 86.29 & 84.72 \\ \hline \hline \end{tabular}
\end{table}