Semantic Image Segmentation for Autonomous Driving Using Fully Convolutional Networks - Kaymak, Cagri; Ucar, Ayegul (Ieee, 2019)
In this paper, an application of semantic image segmentation is implemented in order to support autonomous driving of autonomous vehicles using deep learning based methods. The application is performed by Fully Convolutional Network (FCN) architectures obtained by making changes in Convolutional Neural Network (CNN) architectures. SYNTHIA- San Francisco (SF) is used as the dataset in the experimental studies performed for the application. The experimental studies are conducted using FCN architectures named FCN-AlexNet, FCN-32s, FCN-16s and FCN-8s. Considering these architectures and dataset, this study is carried out for the first time in the literature. The validations of the network models used for experimental studies are compared on the dataset. In addition, segmentation inferences are visualized and thus the segmentation precisions of the FCN architectures are observed. Experimental results are shown that FCNs are suitable for segmentation applications that can assist the autonomous driving of autonomous vehicles. However, it is thought that the experimental results can contribute to the literature and the researchers working on autonomous driving.
International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 21-22, 2019 -- Inonu Univ, Malatya, TURKEY
Keywords: Deep learning; Fully Convolutional Network; Semantic image segmentation; Autonomous driving