%0 Conference Proceedings
%T Driveable Area Detection Using Semantic Segmentation Deep Neural Network
%+ Anna University
%+ SRM Institute of Science and Technology (SRM)
%A Subhasree, P.
%A Karthikeyan, P.
%A Senthilnathan, R.
%Z Part 2: Computational Intelligence for Image and Video Analysis
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 3rd International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Chennai, India
%Y Aravindan Chandrabose
%Y Ulrich Furbach
%Y Ashish Ghosh
%Y Anand Kumar M.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-578
%P 222-230
%8 2020-02-20
%D 2020
%R 10.1007/978-3-030-63467-4_18
%K Road detection
%K Road segmentation
%K Driveable area detection
%K Semantic segmentation
%K Autonomous driving
%Z Computer Science [cs]Conference papers
%X Autonomous vehicles use road images to detect roads, identify lanes, objects around the vehicle and other important pieces of information. This information retrieved from the road data helps in making appropriate driving decisions for autonomous vehicles. Road segmentation is such a technique that segments the road from the image. Many deep learning networks developed for semantic segmentation can be fine-tuned for road segmentation. The paper presents details of the segmentation of the driveable area from the road image using a semantic segmentation network. The semantic segmentation network used segments road into the driveable and alternate area separately. Driveable area and alternately driveable area on a road are semantically different, but it is a difficult computer vision task to differentiate between them since they are similar in texture, color, and other important features. However, due to the development of advanced Deep Convolutional Neural Networks and road datasets, the differentiation was possible. A result achieved in detecting the driveable area using a semantic segmentation network, DeepLab, on the Berkley Deep Drive dataset is reported.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-03434796/document
%2 https://inria.hal.science/hal-03434796/file/507484_1_En_18_Chapter.pdf
%L hal-03434796
%U https://inria.hal.science/hal-03434796
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-578