## 4 Experimental Results and Analysis
### Model Evaluation
For 10 classes of the MI-AUC and State Farm distracted driver detection datasets, an exact and comprehensive measure for the analysis of findings is presented in Figure 5 in the shape of a confusion matrix. It displays the U2-net model's categorization results. Since a large portion of the information is in the matrix's diagonal section, and there are not many misclassification mistakes, Figure 5 demonstrates the U2-net model's strong classification performance. For each class, we compute the Intersection over Union (IoU) using the confusion matrix in the manner described below. Given a confusion matrix \(c\) for N number of classes, the \(IoU_{i}\) for a class \(i\) is given as follows:  
Figure 4: Accuracy and loss percentages for the U2-net model. (**a**,**b**) show the “training and testing loss”, “training and testing accuracy” for the MI-AUC dataset, respectively. (**c**,**d**) show the “training and testing loss”, “training and testing accuracy” for the State Farm data set, respectively. _Appl. Sci._**2023**, _13_, 11898 \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Image Source** & **Approximate No. of Parameters (in Millions) [41]** & **Accuracy** \\ \hline \multirow{8}{*}{AlexNet [13]} & Original & 62 & 93.65\% \\  & Skin Segmented & 62 & 93.60\% \\  & Face & 62 & 86.68\% \\  & Hands & 62 & 89.52\% \\  & Face + Hands & 62 & 86.68\% \\ \hline \multirow{8}{*}{Inception V3 [11]} & Original & 24 & 95.17\% \\  & Skin Segmented & 24 & 94.57\% \\ \cline{1-1}  & Face & 24 & 88.82\% \\ \cline{1-1}  & Hands & 24 & 91.62\% \\ \cline{1-1}  & Face + Hands & 24 & 90.88\% \\ \hline \multirow{2}{*}{Majority Voting Ensemble [11]} & – & 120 & 95.77\% \\  & GA-Weighted Ensemble [11] & – & 120 & 95.98\% \\ \cline{1-1}  & Original VGG [41] & Original & 140 & 94.44\% \\ \cline{1-1}  & VGG with Regularization [41] & Original & 140 & 96.31\% \\ \cline{1-1}  & Modified VGG [41] & Original & 15 & 95.54\% \\ \cline{1-1}  & Original VGG16 [43] & Original & – & 79.86\% \\ \cline{1-1}  & VGG16 one attention [43] & Original & – & 84.82\% \\ \cline{1-1}  & VGG16 two-way attention [43] & Original & – & 87.74\% \\ \cline{1-1}  & DenseNet + Latent + Pose [44] & Original & 8.06 & 94.2\% \\ \cline{1-1}  & MobileNet [41] & Original & 4.2 & 94.67\% \\ \cline{1-1}  & MobileNetV2 [41] & Original & 3.5 & 94.74\% \\ \cline{1-1}  & NasNet Mobile [41] & Original & 5.3 & 94.69\% \\ \cline{1-1}  & SqueezeNet [41] & Original & 1.25 & 93.21\% \\ \cline{1-1}  & Mobile VGG [41] & Original & 2.2 & 95.24\% \\ \cline{1-1}  & D-HCNN [6] & Original & 0.76 & 95.59\% \\ \cline{1-1}  & **U2-net (proposed model)** & Original & 41.6 & **98.34\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training and testing accuracy for the U2-Net compared to other architectures using the MI-AUC dataset [6]. Further, we use the confusion matrix to calculate the _Precision_, _Recall_, _Accuracy_, and _F-score_ for each class \(i\) as follows. Given a confusion matrix \(c\) for N number of classes:  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F\text{-}score=\frac{2*Precision*Recall}{Precision+Recall}=\frac{2*TP}{2*TP+ FP+FN} \tag{7}\]  
Tables 6 and 7 show the class-wise evaluation results for the U2-net model in terms of intersection over union, precision, recall (sensitivity), f-score, and accuracy scores for the testing data using the MI-AUC and State Farm datasets, respectively. It is clear that the evaluation results of the U2-net model using the State Farm dataset achieve better performance results. As we mentioned earlier, this discrepancy results from some erroneous training and testing data labels given in the MI-AUC dataset, whereas the information contained in the State Farm dataset is correctly labeled [41]. Table 8 displays the assessment findings for the most cutting-edge CNN designs using the State Farm dataset in terms of the estimated total number of parameters (in millions) and accuracy. It is important to note that the U2-net paradigm outperforms these designs.