A road segmentation method based on the deep auto-encoder with supervised learning. (May 2018)

• Record Type: Journal Article
• Authors: Song, Xiaona; Rui, Ting; Zhang, Sai; Fei, Jianchao; Wang, Xinqing
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 381
• Page End: 388
• Publication Date: 2018-05
• Subjects: Image segmentation -- Road recognition -- Auto-encoder -- Semantic segmentation -- Unmanned vehicle; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.04.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Road environment perception is a key technique for unmanned vehicles. Segmentation of road images is an important method of determining the driving area. The segmentation precisions of existing methods are not high, and some are not in real-time. To solve these problems, we design a supervised deep auto-encoder (AE) model to complete the semantic segmentation of road environment images. By adding a supervised layer to a classical AE, and using the segmentation image of training samples as the supervised information, the model can learn the useful features to complete the semantic segmentation. Next, the multilayer stacking method of the supervised AE is designed to build the supervised deep AE, since the deep network has more abundant and diversified features. Finally, we verified the method using CamVid. Compared with Convolutional Neural Networks(CNN) and Fully Convolutional Networks(FCN), the road segmentation performance, such as precision and speed were improved.