## 3 The U2-Net Architecture  
In this paper, we adjust and prolong the U-net architecture [5] in a way that yields more precise image classification results. The U-net architecture was initially used to enhance image segmentation. It offers a training technique that greatly hinges on data augmentation and uses the prevailing annotated models well. The characteristics of the U-Net architecture, such as skip connections, fully convolutional design, and efficient training, have made it adaptable to a wide range of computer vision tasks beyond medical imaging. It has been successfully used in applications such as satellite image segmentation, road segmentation in autonomous driving, and more. Generally, the Fully Convolutional Networks (FCNs) enable end-to-end learning for pixel-wise predictions and capture local and global context information through convolutional layers. The U-Net architecture builds on the concept of (FCNs), which has shown promise in image segmentation tasks. The U-shape architecture allows high-level features and fine-grained details. The skip connections help preserve spatial information and enable the model to recover details lost during the down-sampling process and enhance accuracy. U-Net was designed with computational efficiency in mind, making it feasible for real-time or near-real-time applications. This efficiency is crucial in detecting distracted drivers, where quick results can have a significant impact on people who are using the road. In summary, the U-net architecture along with its innovative design and adaptability have made it a widely adopted and influential model in the field of image classification. There are different neural network structures that have been used to detect distracted drivers such as VGGNet [30], ResNet [32], and EfficientNet [33]. However, these structures do not preserve image spatial information nor concatenate image features, which are critical in our task. The strengths of U-Net lie in its suitability for recovering fine-grained details and spatial information [34]. The novelty of the U2-net model are its structure, ability to adapt to different domains, and its ability to customize the loss function. Adding the color information to the model along with creating new different convolutional layers and aggregating them with dense and flattened layers allows the U2-net model to capture distinct visual cues of distracted driving and enhance the classification results. U2-net increases the depth of the whole U-net architecture without significantly increasing the computational cost because of the pooling operations. The original U-net architecture is made up of two paths: expanding and contracting path. The encoder (contracting path) captures the image's context, whereas the uniform, increasing path (the decoder) allows for exact location [5]. The contracting route consists of \begin{table}
\begin{tabular}{l l l l l} \hline
**Reference** & **Method** & **Architecture** & **Dataset** & **Accuracy** \\ \hline
[4] & MOG, 12, dropout, and batch normalization & HOG + CNN & MI-AUC and State-Farm & 95.595,99.987\% \\
[7] & CNN, VGG-18, ResNet50, MobileNetV2 & Translate Learning & State-Farm & 97.45\%, 94.017, 94.28\%, 98.12\%, respectively \\
[11] & Deep Learning & Ensemble CNN & MI-AUC & 90\% \\
[12] & Deep CNN & Inception V3 & MI-AUC & 89.1\% \\
[13] & Drive-Net & CNN with Random Forest & State-Farm & 95\% \\
[17] & TSD-UN and CAOG & Deep Learning & MI-AUC & 88.3\% \\
[20] & Deep CNN & VGG-18 and VCG19 & State-Farm & 98.98\% \\
[24] & DenseNet-GA & Genetic Algorithm with Deep NN & State-Farm & 98.80\% \\
[21] & CAf-CpnNet & Capsule network with attention module and convolution filters & State-Farm and MI-AUC & 99.88\%, 96.78\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison between the related work in terms of architecture, dataset, accuracy, limitation, and research gap. max pooling and convolution layers, whereas the expanding path consists of transpose convolution operation and fully connected layers.