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论文类型：期刊论文

发表时间：2018-05-01

发表刊物：IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

收录刊物：SCIE、Scopus

卷号：19

期号：5

页面范围：1664-1669

ISSN号：1524-9050

关键字：Deep learning; disparity map; traffic scene segmentation

摘要：Semantic segmentation of traffic scenes has potential applications in intelligent transportation systems. Deep learning techniques can improve segmentation accuracy, especially when the information from depth maps is introduced. However, little research has been done on the application of depth maps to the segmentation of traffic scene. In this paper, we propose a method for semantic segmentation of traffic scenes based on RGB-D images and deep learning. The semi-global stereo matching algorithm and the fast global image smoothing method are employed to obtain a smooth disparity map. We present a new deep fully convolutional neural network architecture for semantic pixel-wise segmentation. We test the performance of the proposed network architecture using RGB-D images as input and compare the results with the method that only takes RGB images as input. The experimental results show that the introduction of the disparity map can help to improve the semantic segmentation accuracy and that our proposed network architecture achieves good real-time performance and competitive segmentation accuracy.