SpinNet: Spinning convolutional network for lane boundary detection

作     者：Ruochen Fan Xuanrun Wang Qibin Hou Hanchao Liu Tai-Jiang Mu 

作者机构：Tsinghua UniversityBeijing100084China Nankai UniversityTianjin300350China 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2019年第5卷第4期

页      面：417-428页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Project No.61572264) Research Grant of Beijing Higher Institution Engineering Research Center Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology 

主　　题：object detection lane boundary detection autonomous driving deep learning 

摘      要：In this paper,we propose a simple but effective framework for lane boundary detection,called Spin *** 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 *** 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 *** extract features in narrow strip-shaped fields,we adopt stripshaped convolutions with kernels which have 1×n or n×1 shape in the spinning convolution *** 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 ***,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 *** the network utilizes this information to predict the weights of the curve,to better form the final lane *** framework is easy to implement and end-to-end *** show that our proposed Spin Net outperforms state-of-the-art methods.