Computer vision algorithms have been utilized for 3-D road imaging and pothole detection
for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of …

Potholes and other road surface damages pose significant risks to vehicles and traffic safety.
The current methods of in situ visual inspection for potholes or cracks are inefficient, costly …

Semantic communication has been regarded as a promising technology to serve upcoming
intelligent applications. However, few studies have addressed the problem of resource …

The recent advancements in deep convolutional neural networks have shown significant
promise in the domain of road scene parsing. Nevertheless, the existing works focus …

Introduction The emergence of automated machine learning or AutoML has raised an
interesting trend of no-code and low-code machine learning where most tasks in the …

Aerial object tracking has recently shown great potential in the field of remote sensing.
However, small objects with limited feature information pose a huge challenge to aerial …

Discovering damage in pavement videos is critical for the assessment of pavement
conditions. Unfortunately, existing damage segmentation methods in pavement videos have …

Traditional pothole detection based on two-dimensional images lacks three-dimensional
(3D) quantitative information such as depth and volume, although the high accuracy. In …

Classification and segmentation of point clouds have attracted increasing attention in recent
years. On the one hand, it is difficult to extract local features with geometric information. On …

Many datasets used to train artificial intelligence systems to recognize potholes, such as the
challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road …