基于全卷积神经网络与条件随机场的车道识别方法  

Lane recognition method based on fully convolution neural network and conditional random fields

作　　者：叶子豪 孙锐[1,2] 王慧慧 Ye Zihao;Sun Rui;Wang Huihui(School of Computer and Information,Hefei University of Technology,Hefei,Anhui 230009,China;Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei,Anhui 230009,China)

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]工业安全与应急技术安徽省重点实验室,安徽合肥230009

出　　处：《光电工程》2019年第2期34-45,共12页Opto-Electronic Engineering

基　　金：国家自然科学基金项目(61471154);安徽省科技攻关科技项目(170d0802181)~~

摘　　要：本文针对传统车道识别方法在复杂路面中自适应能力差的特点,基于图像分割技术提出了一种基于全卷积神经网络与条件随机场的车道识别方法。该方法通过大量数据的训练,使神经网络模型可以识别出车道,并且再通过条件随机场使得分割出来的车道覆盖面积及车道边缘的处理更加完善。同时,本文为了解决高速公路中对检测实时性的高要求,设计了一个全卷积神经网络,该网络结构简单,只有13万个参数,并且做出如下三点改进:采用BN算法提高网络的泛化能力及收敛速度;采用了LeakyReLU激活函数取代了一般使用的relu或者sigmoid激活函数,并且采用Nadam作为网络的优化器使得该网络具有更好的鲁棒性;采用条件随机场作为后端处理解决车道边缘处分割不足并且加大了车道覆盖面积。最后本文为了解决城市道路检测中道路环境复杂的问题,利用FCN-16s网络模型加条件随机场的后端处理实现了复杂城市道路的识别。实验证明,在面对高速公路的高速及车道简单环境下,本文设计的网络模型更具有实时性且足够胜任车道的识别。在面对城市道路的复杂环境下,FCN-16s模型加条件随机场更能精确地识别出车道,并在KITTI道路检测基准上取得不错的结果。Aiming at the poor adaptability of traditional lane recognition method in complex pavement,this paper proposes a lane recognition method based on full convolutional neural network and conditional random field,according to image segmentation technology.The method can make the neural network model identify the lanes by training a large amount of data,and then make the segmentation of the lanes'coverage and the lane edges more perfect through the conditional random field.At the same time,in order to solve the high requirement of real-time detection in expressway,a fully convolution neural network is designed in this paper.The network structure is simple with only 130000 parameters and three improvements are made as follows:BN algorithm is used to improve network generalization ability and convergence rate;LeakyReLU activation function is used to replace the commonly used relu or sigmoid activation function,and using Nadam as the network optimizer makes the network have better robustness;Conditional random field is used as the back-end processing solution insufficient lane segmentation and further to increase lane coverage.Finally,in order to solve the problem of complex road environment in urban road testing,this paper uses the back-end processing of FCN-16s network model and conditional random field to realize the recognition of complex urban roads.Experiments show that the network model designed in this paper is more real-time and sufficient for lane identification in the face of high-speed expressways and simple lanes.In the complex environment of urban road,FCN-16s model plus conditional random field can identify lane more accurately and get good results on KITTI road test benchmarks.

关 键 词：车道检测 全卷积神经网络 条件随机场 网络优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]