  Lane mark detection is an important element in the road scene analysis for
Advanced Driver Assistant System (ADAS). Limited by the onboard computing
power, it is still a challenge to reduce system complexity and maintain high
accuracy at the same time. In this paper, we propose a Lane Marking Detector
(LMD) using a deep convolutional neural network to extract robust lane marking
features. To improve its performance with a target of lower complexity, the
dilated convolution is adopted. A shallower and thinner structure is designed
to decrease the computational cost. Moreover, we also design post-processing
algorithms to construct 3rd-order polynomial models to fit into the curved
lanes. Our system shows promising results on the captured road scenes.
