Semantic image segmentation for autonomous driving using fully convolutional networks - Kaymak, Cagri; Ucar, Aysegul (Institute of Electrical and Electronics Engineers Inc., 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. © 2019 IEEE.
Tam Konvolösyonel A?lar Kullanarak Otonom Söröş için Anlamsal Göröntö Bölötleme
2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- Malatya -- 153040
Keywords: Autonomous driving; Deep learning; Fully Convolutional Network; Semantic image segmentation