Smart Pavement: An Attention-Based Classification Model forĀ Road Pavement Material
Paper in proceeding, 2022

Intelligent recognition of traffic road damage is essential for realizing smart vehicles and intelligent transportation systems. The classification of road material types before recognition is a challenge for traffic road damage recognition due to differences in features such as concrete and asphalt. In addition, the widely distributed roads make environmental factors a critical factor affecting the classification. In this paper, we propose a deep learning-based road material classification method that introduces an attention mechanism to deal with the influence of different environments on road material recognition. We acquired tens of thousands of road surface images for training and testing and performed practical validation in real roads. The experiments show that our method has high accuracy and recall in road material classification.

Intelligent transportation

Machine learning

Road engineering

Author

Ye Yuan

Tongji University

Qingwen Xue

Tongji University

Hong Lang

Tongji University

Jie Zhu

Transportgruppen

Jiang Chen

Tongji University

Peng Yuan

China Electronic Technology Group Corporation

Smart Innovation, Systems and Technologies

2190-3018 (ISSN) 2190-3026 (eISSN)

Vol. 304 SIST 133-140
978-981192812-3 (ISBN)

5th KES International Symposium on Smart Transportation Systems, KES STS 2022
Rhodes, Greece,

Subject Categories

Infrastructure Engineering

Vehicle Engineering

Other Civil Engineering

DOI

10.1007/978-981-19-2813-0_14

More information

Latest update

11/25/2022