基于全卷积网络的车道区域分割算法  

Algorithm for lane region segmentation based on fully-convolutional-network

作　　者：魏民祥[1] 滕德成 WEI Minxiang;TENG Decheng(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学能源与动力学院

出　　处：《汽车安全与节能学报》2019年第3期334-341,共8页Journal of Automotive Safety and Energy

摘　　要：为智能车辆的轻量化、实时性检测,提高车道识别的准确度、鲁棒性,提出了一种利用全卷积网络(FCN)实现车道区域分割的方法。采用一种对称结构的全卷积网络对车道区域作逐像素预测:利用卷积、池化提取车道特征,利用池化索引辅助上采样,用卷积来恢复特征信息。在既定网络结构下,比较3×3、5×5和7×7尺寸的卷积核对模型性能的影响。基于FCN-32s和FCN-16s,分别设计混叠结构的FCN和无混叠结构的FCN与本网络作测试对比。结果表明:该算法对车道分割准确、鲁棒性强、实时处理能力优秀,分割效果优于传统FCN;在3种不同尺寸中,小尺寸(3×3)卷积核的实时处理速率最高,达53帧/s。因此,该算法适合自动驾驶道路感知任务。A method of implementing lane region segmentation was proposed by using fully convolutional network(FCN)for lightweight and real-time detection of intelligent vehicles with a high accuracy and robustness of lane recognition.A symmetry-structured fully convolutional network was used to predict the lane area pixel by pixel:the convolution and pooling were used to extract the lane features,and the up sampling wes aided by pooling indices,and convolution were used to recover the feature information.Under the established network structure,the effects of convolution kernels of 3×3,5×5 and 7×7 sizes on the performance of the model were compared.The FCN with skip layers and the FCN without skip layers were compared with the proposed network based on FCN-32s and FCN-16s.The results show that the proposed algorithm is accurate,robust and accurate in real-time processing,and the segmentation is better than traditional FCNs.The small convolution kernel(3×3)method has the best real-time handling speed of 53 frames per second among the three different sizes.Therefore,the proposed algorithm is suitable for road perception for autonomous driving.

关 键 词：智能车辆 车道识别 实时检测 车道区域分割 深度学习 全卷积网络(FCN) 卷积核尺寸 

分 类 号：U461.6[机械工程—车辆工程] U467.1[交通运输工程—载运工具运用工程]