Smart Pavement: An Attention-Based Classification Model for Road Pavement Material

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Infrastructure Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Other Civil Engineering (hsv//eng)

Nyckelord

Intelligent transportation

Machine learning

Road engineering

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