Lane detection through epipole estimation by convolutional neural networks

被引：0
作者：
Kim D.-H. [1 ]
Ha J.-E. [1 ]
机构：
[1] Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology
来源：
Journal of Institute of Control, Robotics and Systems | 2018年 / 24卷 / 09期
关键词：
Convolutional neural networks; Deep learning; Lane detection; Transfer learning;
D O I：
10.5302/J.ICROS.2018.18.0111
中图分类号：
学科分类号：
摘要：
Lane detection is essential in autonomous navigation. Conventional algorithms use hand crafted features which produce difficulties because of diverse image variations from illumination variations, occlusions and shadows. Recently, deep learning based approaches have provided more robust results. In this paper, we present an algorithm for the robust detection of lanes by finding vanishing points with convolutional neural networks. We use two modified CNN architectures, where the final output layer consists of four elements. The epipole and the angles of the current driving lane each have two elements. Experiments are performed by using two modified structures of the NVIDIA end-to-end model[9] and the ResNet-50 model[10]. © ICROS 2018.
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页码：851 / 856
页数：5
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