Common road segmentation methods are often limited by environmental noise and the roughness of the segmenting edges. A road segmentation method was developed to address these shortcomings by combining a fully convolutional neural network and a conditional random field. The feature representation in the neural networks models the road segmentation as a binary classification problem. A VGG-16 deep convolutional neural network based fully convolutional network was constructed to classify each road image end to end into the road and the background. Then, the fully-connected conditional random field (CRF) was used for fine segmentation to refine the coarse edges obtained from the binary classification. Tests of road segmentation benchmark datasets acquired in real environments show that this method can achieve 98.13% segmentation accuracy and real-time processing with 0.84 s perimage.
摘要 常见的道路分割方法往往环境噪声鲁棒性不足并且分割边缘不够平滑。针对该问题, 提出了一种组合全卷积神经网络和全连接条件随机场的道路分割方法。首先, 利用深度神经网络良好的特征表征能力, 将道路分割视为一个二分类问题, 构建一个基于VGG_16深度卷积网络的全卷积网络, 实现道路图像端到端的路面和背景分类; 然后, 利用全连接条件随机场能够实现图像精细分割的特点, 采用全连接条件随机场对二分类得到的粗糙边缘再进行平滑优化。针对真实环境下采集的道路分割基准数据库的测试结果表明：该方法获得了98.13%的分割准确率以及每0.84 s处理1幅图像的分割速度, 具有一定的先进性。