Efficient Dense Spatial Pyramid Network for Lane Detection

Abstract

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.