This paper presents a novel intelligent system based on graph convolutional neural
networks to study road crack detection in intelligent transportation systems. The visual …

Automatic road crack detection is an important transportation maintenance responsibility for
ensuring driving comfort and safety. Manual inspection is considered to be a risky method …

Feature maps of different scales in convolutional neural networks (CNNs) can be regarded
as image pyramids. In classification tasks, only the last layer of feature maps is used for …

The various defects that occur on asphalt pavement are a direct cause car accidents, and
countermeasures are required because they cause significantly dangerous situations. In this …

Traditional image processing algorithms have a lot of limitations when dealing with crack
detection problems. And the effect is not ideal if the classical deep learning model were …

Automatic detection of road cracks is one of the significant aspects of road maintenance
systems. However, it involves a lot of complexities to accurately identify the cracks because …

In recent years, the worldwide road network has grown rapidly and reached an enormous
size. Parallel to this size of the road network, the imperfections on the road surfaces are just …

The presence of road cracks is an important indicator of damage. Deep learning is a
prevailing method for detecting cracks in road surface images because of its detection …

Automatic crack detection on pavement surfaces is an important research field in the scope
of developing an intelligent transportation infrastructure system. In this paper, a cost effective …

Road deteriorations threaten people 'safety as they are considered one of the major causes
of road accidents leading to significant human and material losses. Automatic detection of …