## 5 Experimental Evaluation
### Evaluation Results  
The different model combinations described in Section 3.2.2 are evaluated on the IDD-Lite dataset. The segmentation performance of each model, with various combinations of backbones, was experimented on and analyzed using the mean IoU value and F1 Score, as tabulated in Table 4.  
The tabulated results in Table 4 clearly show that U-net performs the best among the segmentation models. When U-net is chosen as the model for segmentation of unstructured Indian roads, either Efficientnetb7 or Inceptionv3 can be used as the best-performing backbones due to their better performance, with the highest mean IoU values of 66.86% and 68.10%, respectively. VGG19 obtains the second-highest mean IoU value of 65.40% when used with PSPNet. VGG19 can extract wider roads but performs less effectively in complex environments. However, VGG faces challenges in terms of memory and time requirements. Finally, MobilenetV2 only needs to be trained with a few parameters, achieving lower segmentation performance than the other encoders. The highest F1 score of 0.7667 represents perfect precision and recall, called the Dice Similarity Coefficient (DSC). These values of the IoU score and their corresponding losses for training and validation are analyzed using the evaluation curves. Figures 4 and 5 represent the two loss functions for the training and validation phases. They depict that pre-trained encoders converge more quickly than the non-pre-trained encoder models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Evaluation Sores of Models with Different Backbone Architectures**} \\ \hline \multirow{3}{*}{**Model/Backbone**} & \multicolumn{2}{c}{**VGG19**} & \multicolumn{2}{c}{**Resnet50**} & \multicolumn{2}{c}{**Efficientnetb7**} & \multicolumn{2}{c}{**InceptionV3**} & \multicolumn{2}{c}{**MobilenetV2**} \\ \cline{2-10}  & **IoU** & **F1 score** & **IoU** & **F1 score** & **IoU** & **F1 score** & **IoU** & **F1 score** \\ \hline U-NET & 0.5332 & 0.6532 & 0.6535 & 0.7667 & 0.6686 & 0.7002 & 0.6810 & 0.7053 & 0.6414 & 0.7623 \\ LinkNet & 0.5256 & 0.6400 & 0.5362 & 0.6388 & 0.6617 & 0.6728 & 0.6031 & 0.6457 & 0.5294 & 0.6234 \\ FPN & 0.6085 & 0.6500 & 0.5424 & 0.6978 & 0.5823 & 0.6964 & 0.5784 & 0.6874 & 0.5280 & 0.6547 \\ PSPNet & 0.6540 & 0.7088 & 0.5232 & 0.7284 & 0.5701 & 0.7003 & 0.5255 & 0.7665 & 0.4909 & 0.6832 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of different backbone architectures for SSM using mean IoU value and F1 score. Thus, after analyzing the performance of the different CNNs in each segmentation model, we can conclude that each encoder classifies different class labels with reasonable accuracy. In order to form the final meta-learning model, a stacking-based ensembling procedure is performed to consolidate the advantage of using each encoder for different class labels. The final segmentation mask generated by this meta-model outperforms the other non-ensembled models. Their mean IoU value evaluates the performance of the final four generated meta-learning models, and these are tabulated in Table 5.  
The higher the mean IoU value, the better the prediction accuracy of the segmentation mask. Therefore, it is evident that the weight-ensembled U-Net has higher segmentation accuracy with a higher mean IoU value. Thus, the proposed ensembled U-Net architecture  
Figure 4: Jaccard Index (IoU Score) value for training and validation datasets for four Segmentation Models with different backbones. (**a**) U-Net; (**b**) Linknet; (**c**) PSPNet; and (**d**) FPN. has achieved satisfactory results in an unstructured environment with high-traffic areas. Since mIoU returns a single value for all the seven classes present, IoU is validated for each class for the ensembled model on the test dataset. The results are also presented in Table 5.  
It is observed from the table that the IoU value for some classes, such as non-drivable areas and road objects, is low due to the uncertain view of both classes in the IDD-Lite dataset.