# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
## 2 Materials and Methods
### U-Net Architecture  
Semantic image segmentation is a fundamental problem in computer vision, with extensive applications in various fields such as medicine, autonomous driving, facial recognition, etc. One of the most popular architectures used for this task is U-Net, proposed by [PERSON] et al. for medical image segmentation [PERSON] et al. (2019). U-Net is a convolutional neural network (CNN) with a symmetric encoding-decoding architecture, capable of achieving accurate segmentation of objects in an image through a combination of convolution and deconvolution layers (Figure 4). Due to its structure, U-Net can capture both local and global features, which makes it extremely efficient for segmentation tasks where fine details are essential [PERSON] et al. (2019), and in general the U-Net architecture consists of two main components: the encoder (contracting path) part and the decoder (expanding path) part.  
The encoding part (contracting path) is responsible for mapping the features in the image. The encoding process is performed through a series of convolution and pooling operations, with the following steps. Two successive convolutions of size 3 \(\times\) 3 pixels are applied, each followed by a ReLU activation function. Max-pooling with a 2 \(\times\) 2 window is used to reduce the spatial dimension of the image while preserving the essential features. At each level, the number of convolutional filters is doubled to retain more complex information (e.g., 64 \(\rightarrow\) 128 \(\rightarrow\) 256 filters). The decoding part (expanding path) is responsible for reconstructing the segmentation at the original image size. It uses up-sampling and convolution layers to increase the spatial dimension of the image. The feature maps from the encoder and decoder are concatenated, which allows the fine details that were lost in the pooling step on the encoding side to be preserved [PERSON] et al. (2019). Two successive 3 \(\times\) 3 convolutions are applied, each followed by ReLU activation, and the last layer applies a 1 \(\times\) 1 convolution to reduce the number of channels to the desired number of classes. With this approach, the U-Net architecture combines global information (high levels of the encoder) with local information (fine details preserved through skip connections), ensuring accurate segmentation.  
It should be noted that in addition to the two main components, an innovative element of U-Net is the use of skip connections, which connect the corresponding layers between  
Figure 3: Examples of environmental scenarios used and generated in Carla: (**a**) highway during the day and cloudy; (**b**) rain, urban; (**c**) night, urban; (**d**) day, clear sky, suburb.  
the encoding and decoding structures. These connections help to preserve fine details (which would otherwise be lost due to pooling operations) and speed up the training process, reducing the learning difficulty of image reconstruction [32] and improving the segmentation of small objects (since information from high resolutions is efficiently reused). U-Net is one of the most popular architectures for semantic segmentation due to its ability to combine information at different levels and preserve fine details of objects.