## 3 Results and Discussion  
The RGB and depth image masks were overlayed on the mask of the respective ground truth segmented images. The true positive (TP) and negative (TN) predictions were compared to false positive (FP) and false negative (FN) predictions to determine the efficacy of its use for segmentation (Table 1). It was clear that in both MCCs and IoU, the RGB meshed point cloud segmentation did not yield promising results for segmentation in 3D, evidenced by each model performing under 82% on both IoU and MCCs when compared to the models segmenting depth maps, performing consistently above 92%. Furthermore, the statistical analysis through multiple mean comparisons showed the separation between RGB and the depth-based networks, showing a statistically significant difference between them (\(p\) < 0.001). In assessing the models based on the framework, the Detectron2 models each displayed no significant differences in IoU and MCCs within the respective spatial domains (2D and 3D) (Table 1). There were, however, significant differences between each of the models based on processing time, where the lightweight ResNet50 was significantly faster than both the ResNet101 and ResNeXt101 models, with the ResNet101 model only being significantly faster than ResNeXt101 in the depth map prediction medium. Like the Detectron2 models, within each spatial domain, the YOLO variants showed no difference in MCCs and IoU. Both the YOLO and Detectron2 models achieved significantly superior performance using the depth maps as the prediction medium across each of the performance metrics. As a qualitative evaluation, the losses were visualized by overlaying the ground truth and predicted masks color-coded based on pixel correctness (TN, TP, FN, FP) (Figures 4 and 5). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Model** & **IoU** & **MCC** & **Processing Time (ms)** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Coul**  
In the qualitative analysis of model performance, the segmentation masks generated by the various YOLO and Detectron2 models were compared to the ground truth masks (Figures 4 and 5), focusing on the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, each distinguished by colors: green, blue, red, and orange, respectively (Figures 4 and 5). The visual aspect of color-coded segmentation outputs, as presented in Figures 4 and 5, provided a powerful tool for evaluating model performance. By assigning distinct colors to the categorized pixels, the visual overlays offered an intuitive segmentation mask. Figure 4: Visualization of segmentation mask correctness of YOLO masks for ToF 3D camera and 2D RGB camera, with true positive as green, true negative as blue, false positive as red, and false negative as orange. Figure 5: Visualization of segmentation mask correctness of Detectron2 masks for ToF 3D and 2D RGB cameras, with true positive as green, true negative as blue, false positive as red, and false negative as orange. and immediate understanding of segmentation accuracy. This approach allowed for error pattern identification, such as areas with high misclassification rates, and assessed the smoothness and precision of segmentation boundaries. This comparison revealed distinct patterns and errors in each model's performance. The errors associated with the RGB level segmentations could be primarily attributed to the camera lens warp causing misalignment as the lens used in this study had a recorded maximum distortion of \(-\)20.43%. Both the YOLOv8-seg and Detectron2 models tended to under-segment the background imagery, leading to more FPs proportionally. The Detectron2 models' under-segmentation in comparison to the YOLO models was less smooth, where the images segmented by the Detectron2 model had jagged and irregular boundaries between segments, indicating that the model struggled to create clean and precise edges (Figure 5).