论文标题
在距离透视变压器层的距离处检测车道和道路标记
Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers
论文作者
论文摘要
准确检测车道和道路标记对于智能车辆而言非常重要。在现有方法中，检测准确性通常会随着距离的增加而降低。这是由于以下事实：遥远的车道和道路标记在图像中占据了少数像素，而在各个距离和透视上，车道和道路标记的尺度都不一致。反视角映射（IPM）可用于消除视角失真，但是固有的插值可以导致伪影，尤其是在遥远的车道和路标周围，因此对车道标记检测和分段的准确性产生了负面影响。为了解决这个问题，我们在完全卷积的网络中采用编码器架构体系结构，并利用空间变压器网络的想法来引入一种新颖的语义分割神经网络。这种方法将IPM过程分解为多个连续的可区分变换层，这些层称为“透视变压器层”。此外，插值特征图通过随后的卷积层进行完善，从而降低了伪影并提高精度。在两个公共数据集上验证了所提出方法在车道标记检测中的有效性：Tusimple和Apolloscape
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
