## 6 Conclusions  
In this work, the proposed weight-ensembled method in semantic segmentation models has enhanced the model performance in pixel-level segmentation. This work mainly focuses on improving the model performance in pixel-level segmentation by using weights to ensemble the output predictions obtained from different base models. The Grid Search algorithm learns the optimal weight vector used in the linear combination of output predictions. This algorithm evaluates the model and generates the optimal weights using cross-validation and the Least Square error method. The optimal weight vectors obtained  
Figure 6: Results from our four weight-ensembled Segmentation Models in the IDD-Lite dataset. (**a**) Different input images to the network representing various conditions from an unstructured environment; (**b**) ground truth values for the input data; (**c**) predicted segmentation results in U-Net\({}_{\text{weight-ensembled}}\), with different colours indicating different classes in the image; (**d**) LinkNet\({}_{\text{weight-ensembled}}\) prediction; (**e**) PSPNet\({}_{\text{weight-ensembled}}\) prediction; and (**f**) FPN\({}_{\text{weight-ensembled}}\) prediction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class/Model**} & \multicolumn{6}{c}{**Evaluation Scores of Ensembled Methods**} \\ \cline{2-9}  & \multicolumn{2}{c}{**U-Net\({}_{\text{weight-Ensembled}}\)**} & \multicolumn{2}{c}{**LinkNet\({}_{\text{weight-Ensembled}}\)**} & \multicolumn{2}{c}{**FPN\({}_{\text{weight-Ensembled}}\)**} & \multicolumn{2}{c}{**PSPNet\({}_{\text{weight-Ensembled}}\)**} \\ \cline{2-9}  & **IoU** & **F1 Score** & **IoU** & **F1 Score** & **IoU** & **F1 Score** & **IoU** & **F1 Score** \\ \hline Class 1 & 0.9215 & & 0.8015 & & 0.78 & & 0.72 & \\ Class 2 & 0.6112 & & 0.5702 & & 0.55 & & 0.45 & \\ Class 3 & 0.6004 & & 0.462 & & 0.451 & & 0.45 & \\ Class 4 & 0.9018 & & 0.852 & 0.652 & & 0.752 & 0.69 & 0.88 & \\ Class 5 & 0.7082 & & 0.68 & & 0.66 & & 0.69 & \\ Class 6 & 0.8815 & & 0.856 & & 0.725 & & 0.80 & \\ Class 7 & 0.7172 & & 0.6812 & & 0.66 & & 0.69 & \\ Mean score & 0.7312 & & 0.7052 & & 0.6552 & & 0.6875 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IoU Value and F1 score obtained by the ensembled Segmentation Models. are applied to the output predictions of the model, and these weighted predictions from base models are ensembled to form the four meta-learning algorithms. The final results from the weight-ensembled U-Net show a significant improvement in predicting the pixel-level information for scene perception, with a higher IoU value of 73.12%. This method has outperformed existing baseline models regarding detection accuracy and memory requirements. Although the primary goal of this work was to implement it on mobile devices, we discovered that it could also be applied to high-end GPUs. However, this approach faces challenges in selecting appropriate backbone networks for feature extraction and in the strategies used to combine the base learner's predictions. Finding the criteria for choosing the most appropriate backbones for the base learner and replacing the basic grid search algorithm with Deep-Learning techniques for faster convergence in training will be the subjects of future work. Future research may also attempt to implement this approach in the IDD dataset, which has more classification problems in each image. We also anticipate getting a similar model performance when applied to high-resolution images with a larger dataset. Conceptualization, S.S.; methodology, S.S.; formal analysis, S.S.; investigation, D.B.G. ; supervision, D.B.G. ; writing--original draft, S.S.; writing--review and editing, S.S. All authors have read and agreed to the published version of the manuscript. The APC is funded by Vellore Institute of Technology, Vellore-632014, India. Not Applicable.