Ling Ding1, 2, Huyin Zhang1, *, Jinsheng Xiao3, *, Cheng Shu3, Shejie Lu2
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 1039-1053, 2020, DOI:10.32604/cmes.2020.08268
Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as
the basis of convolutional neural network to extract lane line features by cavity
convolution, wherein the lane lines are divided into dotted lines and solid lines.
Expanding the field of experience through hollow convolution, the full connection layer
of the network is discarded, the last largest pooling layer of the VGG16 network is
removed, and the processing of the last three convolution layers is replaced by hole
convolution. At the same time, CNN adopts the encoder and decoder structure mode, and
uses the index function of the… More >
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