## 3 Experimental Results
### Performance Evaluation of Object Detection under Varying Brightness Levels  
To evaluate the performance of our proposed model, we compared it with state-of-the-art models that fuse RGB cameras and LiDAR data and share similar architectures. The comparative results are tabulated in Table 1. The experimental results indicate that the RGB-LiDAR fusion models, which represent the most common architecture, and the novel model proposed in this study achieved high performance levels during daytime scenarios, with the brightness set to 100%. No significant performance gap was observed between the RGB-LiDAR fusion models and the proposed model. However, the performance of both models gradually deteriorated as the lighting conditions worsened, primarily due to the RGB camera's hypersensitivity to low light. When the image brightness was reduced to 40% of its original level, the proposed model demonstrated a slightly higher average AP compared to the baseline model, with a performance gap of approximately 1.5%. We also compared our proposed model with models that apply preprocessing filters to depth maps with a similar architecture, such as [PERSON] and Pessoa [12], Deng [13], and Ali and Clausi [14]. It was found that all models perform well in daylight and do not seem to differ significantly, while in darkness, our proposed model shows 1-2% higher APs and a relatively small improvement in detection accuracy. Figure 5 shows an example of pedestrian detection results in daylight, i.e., when the brightness level is 100%. In the figure, the white bounding box indicates the ground truth, the blue box indicates a successful pedestrian detection, and the thick yellow box indicates a missed detection. Figure 5a shows the result of detecting pedestrians using only the depth map obtained from LiDAR, while Figure 5b shows the result of fusing LiDAR with an RGB camera to detect pedestrians. Figure 5c-f show the detection results of the [PERSON] and Pessoa [12] model, which sequentially accentuates the contours of objects with the Dilation filter; the [PERSON] [13] model, which emphasizes the density of objects with the unsharp mask filter; the [PERSON] and [PERSON] [14] model applied with the Canny edge filter; and the proposed model, respectively. As can be seen, when the illumination was sufficiently high, both existing models with similar architectures detected most of the objects except for one or two, and no substantial difference was observed in their performance. However, a closer inspection reveals that the proposed model distinctly identifies a pedestrian located centrally in the scene, which is overlooked by all other methods. The proposed model's superior reconstruction quality facilitates more accurate detection. To evaluate the effectiveness of the proposed model for detecting objects in low-light environments, we conducted a pedestrian detection experiment by artificially manipulating the image to decrease its brightness to <40% of the brightness of the original image; an example of the results is shown in Figure 6. The false alarms represented by the thick pink boxes shown in Figure 6a,b were generated by models that did not apply a sharpening filter to the depth map, which can be attributed to the difficulty of distinguishing inaccurate pedestrian shapes with the depth map alone, or to the fact that the camera's role considerably decreases with decreasing illumination. However, Figure 6c-f show that by sharpening the objects with a preprocessing filter on the depth map, the false alarm