## 1 Introduction and Background
Deep Neural Networks (DNN) are an important topic in the field of artificial intelligence. Image and video processing uses AI and ML in several applications, such as in object detection. Using AI, ML, and DNN algorithms and techniques for object detection increases the accuracy of the detection and expands its applications. Deep learning can play a significant role by detecting the distracted driver activity and notifying the driver to stop while these activities occur to prevent accidents. While U-Net may not be a suitable choice for object tracking on its own, it is worth mentioning that features extracted by U-Net architecture can be valuable for object detection tasks [4]. Object tracking requires algorithms and architectures that are specifically designed to handle the temporal continuity and identity maintenance of objects across video frames. The U2-net architecture does not handle the temporal information and object identity across video frames. However, considering the temporal continuity of objects between frames will be considered when dealing with videos, and represents a future work that is outside the scope of this research. In this research, we develop a model called U2-net that automatically detects distracted drivers, especially those who are using mobile phones. The U2-net is a modified version of the original U-net architecture. U-Net is a convolutional neural network (CNN) architecture that consists of an encoding path and a corresponding decoding path, with skip connections that bridge the encoding and decoding paths at multiple resolutions. The encoding path is a series of convolutional and pooling layers that gradually reduce the spatial resolution of the input image, while the decoding path consists of transposed convolutional layers that progressively up-sample the feature maps to the original resolution. The skip connections combine the low-level feature maps from the encoding path with the high-level feature maps from the decoding path to capture both local and global information in the image. U-Net is a 2D CNN architecture that is widely used for image segmentation [4; 5]. We modify the original U-net architecture by adding four convolution layers, two max pooling layers, and dropout layers to the contracting path. additionally, in the expanding path, we add four transpose convolution layers, two max pooling layers, and dropout layers. These new layers add more spatial information and context to the classifier and avoid model overfitting. Moreover, we use full-color images to provide more information to the classifier and educ the size of each input image to reduce the training time and minimize the model complexity. The dense layer is used to predict the output result that is formed by a weighted average function of its input. The U2-net model provides deep layers to represent image features and yields more precise classification results. The motivation behind this model is that it captures the context around the objects in order to provide a better object representation and classification. The U2-net uses input images collected from cameras that capture frontal views from inside the vehicle. Many existing methods have some limitations, such as being locally integrated inside the vehicle, and therefore cannot prohibit distracted drivers. Additionally, their accuracy drops if the driver changes his behavior, Moreover, they do not provide concrete evidence. The U2-net model addresses some of these limitations by providing a dynamic and adaptive system that provides efficient results regardless of the driver behavior. It achieves 2.75% higher accuracy results than the D-HCNN [6] on the MI-AUC dataset and 1.52% higher accuracy results than the MobileNetV2 [7] on the State Farm dataset. The U2-net model can also help increase the drivers' awareness of their driving habits and associated risks, promote safe driving practices, and help vehicle manufacturers improve the safety levels in vehicles and minimize the driver distraction.