Semantic segmentation, a pivotal problem in computer vision, involves assigning labels to every pixel in an image to facilitate scene understanding, crucial for applications like autonomous vehicles and virtual reality. The article delves into various approaches for semantic segmentation using deep learning, with a focus on Fully Convolutional Networks (FCNs) which enable pixel-level predictions by eliminating region proposals and utilizing convolutional layers to process arbitrarily sized images. It highlights the significance of pre-trained networks like VGG and ResNet as encoders and explores methods such as region-based approaches, FCNs, and weakly supervised methods, while addressing challenges like preserving spatial details and reducing annotation costs. A step-by-step guide using TensorFlow illustrates the implementation of an FCN for road detection, leveraging techniques such as transposed convolutions for upsampling, skip connections for refining predictions, and optimization through cross-entropy loss and Adam optimizer, culminating in a training process that demonstrates improved results through parameter tuning.