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Conference Papers Year : 2020

Driveable Area Detection Using Semantic Segmentation Deep Neural Network

P. Subhasree
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P. Karthikeyan
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Abstract

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.
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hal-03434796 , version 1 (18-11-2021)

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P. Subhasree, P. Karthikeyan, R. Senthilnathan. Driveable Area Detection Using Semantic Segmentation Deep Neural Network. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.222-230, ⟨10.1007/978-3-030-63467-4_18⟩. ⟨hal-03434796⟩
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