  Accurate detection of lane and road markings is a task of great importance
for intelligent vehicles. In existing approaches, the detection accuracy often
degrades with the increasing distance. This is due to the fact that distant
lane and road markings occupy a small number of pixels in the image, and scales
of lane and road markings are inconsistent at various distances and
perspectives. The Inverse Perspective Mapping (IPM) can be used to eliminate
the perspective distortion, but the inherent interpolation can lead to
artifacts especially around distant lane and road markings and thus has a
negative impact on the accuracy of lane marking detection and segmentation. To
solve this problem, we adopt the Encoder-Decoder architecture in Fully
Convolutional Networks and leverage the idea of Spatial Transformer Networks to
introduce a novel semantic segmentation neural network. This approach
decomposes the IPM process into multiple consecutive differentiable homographic
transform layers, which are called "Perspective Transformer Layers".
Furthermore, the interpolated feature map is refined by subsequent
convolutional layers thus reducing the artifacts and improving the accuracy.
The effectiveness of the proposed method in lane marking detection is validated
on two public datasets: TuSimple and ApolloScape
