Convolutional Neural Network Applied to Traversability Analysis of Vehicles. (12th November 2013)

• Record Type: Journal Article
• Authors: Linhui, Li; Mengmeng, Wang; Xinli, Ding; Jing, Lian; Yunpeng, Zong
• Other Names: Wang Fenyuan Academic Editor.
• Is Part Of: Advances in mechanical engineering. Volume 2013(2013)
• Publication Date: 2013-11-12
• Subjects: Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1155/2013/542832
• Languages: English
• ISSNs: 1687-8132

Abstract

Abstract : We focus on the need for traversability analysis of vehicles with convolutional neural networks. Most related approaches to traversability analysis of vehicles suffer from the limitations imposed by extracting explicit features, algorithm scalability, and environment adaptivity. In views of this, an approach based on the convolutional neural network (CNN) is presented to traversability analysis of vehicles, which can extract implicit features. Besides, in order to enhance the training speed and accuracy, preprocessing and normalization are adopted before training. The experimental results demonstrate that our method achieves high accuracy and strong robustness.