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RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)

Abstract

Accurately detecting road and its boundary on the images is an essential task for vision-based autonomous driving systems. However, prevailing methods either only detect road or add an extra processing stage to detect road boundary. In this work, we introduce a deep neural network, called Road and road Boundary detection Network (RBNet), that can detect both road and road boundary in a single process. In specific, we first investigate the contextual relationship between the road structure and its boundary arrangement and then model them with a Bayesian network. By implementing the Bayesian model, the RBNet can learn to simultaneously estimate the probabilities of a pixel on the image belonging to the road and road boundary. Comprehensive evaluations are carried out based on the well-known road benchmark, which can demonstrate the compelling performance of the proposed method.

Keywords

Deep learning Deep convolutional neural network Road detection Boundary detection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Technology, UBTECH Sydney Artificial Intelligence CentreThe University of SydneyDarlingtonAustralia
  2. 2.Faculty of Engineering and Information Technology, Centre for Artificial IntelligenceUniversity of Technology SydneyUltimoAustralia

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