In this paper, we propose a simple but effective framework for lane boundary detection, called SpinNet. Considering that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive, therefore, analyzing and collecting global context information is crucial for lane boundary detection. To this end, we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global perspective. To extract features in narrow strip-shaped fields, we adopt strip-shaped convolutions with kernels which have 1 × n or n × 1 shape in the spinning convolution layer. To tackle the problem of that straight strip-shaped convolutions are only able to extract features in vertical or horizontal directions, we introduce the concept of feature map rotation to allow the convolutions to be applied in multiple directions so that more information can be collected concerning a whole lane boundary. Moreover, unlike most existing lane boundary detectors, which extract lane boundaries from segmentation masks, our lane boundary parameterization branch predicts a curve expression for the lane boundary for each pixel in the output feature map. And the network utilizes this information to predict the weights of the curve, to better form the final lane boundaries. Our framework is easy to implement and end-to-end trainable. Experiments show that our proposed SpinNet outperforms state-of-the-art methods.

中文翻译：

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SpinNet：旋转卷积网络用于车道边界检测

在本文中，我们提出了一个简单但有效的车道边界检测框架，称为SpinNet。考虑到汽车或行人经常会遮挡车道边界，并且车道边界的局部特征不明显，因此，分析和收集全局上下文信息对于车道边界检测至关重要。为此，我们在网络中设计了一个新颖的旋转卷积层和一个全新的车道参数化分支，以从全局角度检测车道边界。为了提取窄带状字段中的特征，我们采用带状卷积，其核具有1× n或n旋转卷积层中的×1形状。为了解决直条形卷积只能提取垂直或水平方向上的特征的问题，我们引入了特征图旋转的概念，以允许在多个方向上应用卷积，以便可以收集有关整个车道边界。而且，与大多数现有的车道边界检测器不同，该检测器从分割蒙版中提取车道边界，我们的车道边界参数化分支为输出特征图中的每个像素预测了车道边界的曲线表达式。网络利用这些信息来预测弯道的权重，以更好地形成最终的车道边界。我们的框架易于实施且可进行端到端培训。