3D pavement data decomposition and texture level evaluation based on step extraction and Pavement-Transformer. (January 2022)

• Record Type: Journal Article
• Authors: Chen, Hongjia; Zhang, Dejin; Gui, Rong; Pu, Fangling; Cao, Min; Xu, Xin
• Is Part Of: Measurement. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: 3D pavement texture -- Signal decomposition -- Transformer -- Step signal extraction -- Texture evaluation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110399
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Pavement texture evaluation is important for driving both skid resistance and pavement maintenance. Limited by the requirements of automation, efficiency and data coverage requirements, most pavement methods focus on damaged areas and static measurement environment. However, maintenance work is practically performed on the entire pavement rather than only the damaged areas, thus a gap between theory and practice is observed. In this study, we designed an efficient texture decomposition method based on the proposed step signal extraction algorithm, which can overcome road fluctuations and accurately extract pavement texture. The Pavement Transformer is introduced for fine texture evaluation, and can better serve pavement maintenance in practice. We conducted experiments on 22, 800 pieces of 3D laser scanning data. The results demonstrate that our decomposition method has improved accuracy and stability. Moreover, the classification accuracy of texture level evaluation is 95.2%, which is better than that of the Vision Transformer. Graphical abstract: Highlights: A 3D pavement data decomposition method with high accuracy and stability is proposed. The step signal extraction algorithm can efficiently separate Step signal. This paper improves the Transformer and proposes the Pavement-Transformer. The method can evaluate the texture level with 95% accuracy, which is higher than ViT.