Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers
This addresses a critical safety issue for autonomous vehicles by enhancing detection of distant markings, though it is an incremental improvement over existing methods.
The paper tackles the problem of degraded detection accuracy for distant lane and road markings in intelligent vehicles by introducing a novel semantic segmentation neural network with Perspective Transformer Layers, achieving improved accuracy validated on TuSimple and ApolloScape datasets.
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