  Deep learning is a fast-growing machine learning approach to perceive and
understand large amounts of data. In this paper, general information about the
deep learning approach which is attracted much attention in the field of
machine learning is given in recent years and an application about semantic
image segmentation is carried out in order to help autonomous driving of
autonomous vehicles. This application is implemented with Fully Convolutional
Network (FCN) architectures obtained by modifying the Convolutional Neural
Network (CNN) architectures based on deep learning. Experimental studies for
the application are utilized 4 different FCN architectures named
FCN-AlexNet,FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are
first trained separately and validation accuracies of these trained network
models on the used dataset is compared. In addition, image segmentation
inferences are visualized to take account of how precisely FCN architectures
can segment objects.
