## 4 Experimental Results and Analysis
### Model Training  
Each dataset is used to train the U2-net design. A total of 80% and 20% of the overall sample amount comprises training and test specimens from each dataset, which are randomly selected. The quantity of images used for model testing and training across the two datasets is displayed in Table 3. Each input image is subjected to a set of filters in the U2-net design, which produces a feature map that lists the existence of any identified features. The U2-Net is designed for detecting distracted drivers without using any pre-trained backbones from image classification. Hence, all of our convolutional layers are initialized and trained from scratch to achieve competitive performance. One significant change we made to the original U-net design was to have the network transmit numerous feature channels to higher-density layers during the increased sampling process. These layers could learn the model and produce a more accurate final result. The U2-net architecture has a fully connected dense layer following the final convolution layer, as shown in Figure 1, which normalizes the data in the feature space and displays the classification results as probabilities, unlike the original U-net architecture, which only utilizes the legitimate part of each convolution. We use the the Softmax activation function as logistic regression layer \begin{table}
\begin{tabular}{l l l} \hline \hline
**Class** & **Number of Images** & **Dataset1-MI-AUC [11]** & **Dataset2-State Farm [14]** \\ \hline C0 & Dataset 1: 2986 Dataset 2: 2489 & \\ \hline C1 & Dataset 1: 1256 Dataset 2: 2267 & \\ \hline C2 & Dataset 1: 1718 Dataset 2: 2317 & \\ \hline C3 & Dataset 1: 1718 Dataset 2: 2346 & \\ \hline C4 & Dataset 1: 1124 Database 2: 2326 & \\ \hline C5 & Dataset 1: 1123 Database 2: 2312 & \\ \hline C6 & Dataset 1: 1076 Database 2: 2325 & \\ \hline C7 & Dataset 1: 1044 Database 2: 2002 & \\ \hline C8 & Dataset 1: 1034 Database 2: 1911 & \\ \hline C9 & Dataset 1: 1797 Database 2: 2129 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample images from the two datasets used in this study. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Training Data** & **Testing Data** & **Total** \\ \hline State Farm [14] & 17,829 & 4458 & 22,278 \\ \hline MI-AUC [11] & 8444 & 2111 & 10,555 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of images used for training and testing. to achieve classification, which is based on the principle of regression, and its loss function is a probabilistic model considering global data. It normalizes the data in the feature space and presents the classification results in the form of probabilities. Each neuron in the final dense layer in U2-net model has the value \(Y\)= \(Y_{1}\), \(Y_{2}\), \(Y_{3}\),..., \(Y_{N}\) as a vector, where \(Y_{i}\) is the possibility that the input image \(Y\) fits to a class \(i\). The highest probability value is selected as a label of the input image \(Y\). Note that the total number of clusters is N = 10. Adam optimizer is used to train the U2-net network and its hyper parameters are set to default (initial learning rate lr = 1 \(\times\)10\({}^{-3}\), betas = (0.9, 0.999), eps = 1 \(\times\)10\({}^{-8}\), weight decay = 0). We train the network at a batch size of 64, verbose = 1, and epochs = 50 until the loss converges without using the validation set, which follows the original U-net model. The training loss converges at early stopping (mode = max) and the whole training process takes about 20 h.  
For the convolution operation, the kernel size is 3 \(\times\) 3, number of filters = (16, 32, 64, 128, 256, 512, 1024), stride = 2, padding = same, activation function ='relu', kernel initializer = 'he_normal'. For the transpose convolution operation, filters = (512, 256, 128, 64, 32, 16), kernel size = 2 \(\times\) 2, strides = (2,2), padding = same. To minimize the processing complexity, the pooling layer (stride = 2 and pool size of 2 \(\times\) 2 with no padding) is in charge of lowering the size of the recovered vectors and dimensionality.