基于卷积神经网络的交通场景语义分割方法研究

摘要

In order to improve the semantic segmentation accuracy of traffic scene, a segmentation method was proposed based on RGB-D image and convolutional neural network. Firstly, on the basis of semi-global stereo matching algorithm, the disparity map was obtained, and the sample library was established by fusing the disparity map D and RGB image in-to the four-channel RGB-D image. Then, with two different structures, the networks were trained by using two different learning rate adjustment strategy respectively. Finally, the traffic scene semantic segmentation test was carried out with RGB-D image as the input, and the results were compared with the segmentation method based on RGB image. The ex-perimental results show that the proposed traffic scene segmentation algorithm based on RGB-D image can achieve high-er semantic segmentation accuracy than that based on RGB image.%为提高交通场景的语义分割精度,提出一种基于 RGB-D 图像和卷积神经网络的分割方法.首先,基于半全局立体匹配算法获取视差图D,并将其与RGB图像融合成四通道RGB-D图像,以建立样本库;其次,对于2种不同结构的卷积神经网络,分别采用2种不同的学习率调整策略对网络进行训练;最后,对训练得到的网络进行测试及对比分析.实验结果表明,基于RGB-D图像的交通场景语义分割算法得到的分割精度高于基于RGB图像的分割算法.