基于SegNet的非结构道路可行驶区域语义分割

为了增强自动驾驶车辆对非结构化道路中可行驶区域的场景理解能力,基于SegNet深度学习网络结构,提出了一种针对非结构道路的可行驶区域语义分割方法。在传统的卷积神经网络基础上,构建编码-解码深度卷积神经网络,用于自动习得图片中非结构化道路的特征,通过在数据集上进行训练和学习,得到图像语义分割模型,可直接用该模型预测非结构道路中的可行驶区域,实现自动驾驶车辆在非结构道路中行驶时的环境感知。实验结果表明,研究方法分割效果和精确度提升明显,Dice相似度和Jaccard相似系数均可达80%以上。

The semantic segmentation of driving regions on unstructured road based on segnet architecture

To improve the autonomous vehicle''s ability to understand the scene of unstructured road driving regions, a semantic segmentation method of unstructured road for autonomous vehicle based on SegNet architecture is proposed. Deep convolutional encoder-decoder architecture is formed by traditional convolutional neural networks, and it can learn the feature map of unstructured roads automatically. By learning and training in the datasets, image semantic segmentation model can be acquired and used to predict the feasible driving area of unstructured roads directly, which is important for autonomous vehicle''s scene understanding. The proposed approach outperforms in precision and segmentation consequent, while Dice coefficient reaches more than 80%.