Pavement, as a kind of common public transit infrastructure, plays an important part in the
daily passing and transportation-associated activities. The good conditions and smooth …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

The ability to perform pixel-wise segmentation on pavement cracks in real-time is paramount
in road service condition assessment and maintenance decision-making practices. Recent …

Deep learning plays an important role in crack segmentation, but most work utilize off-the-
shelf or improved models that have not been specifically developed for this task. High …

The integration of visible and thermal images has demonstrated the potential ability to
enhance crack segmentation accuracy. However, due to the intricate texture of masonry …

Due to the rising cost of human resources in road maintenance and the pursuit of efficiency,
autonomous road inspection vehicles are developed for intelligent detection of road disease …

Pavement cracks can result in the degradation of pavement performance. Due to the lack of
timely inspection and reparation for the pavement cracks, with the development of cracks …

Crack detection in civil infrastructures has seen impressive accuracy achieved by
Convolutional Neural Networks (CNNs) and Transformers. However, practical deployments …

Crack is a condition indicator of the pavement's structure. Generally, crack detection is an
essential task for effective diagnosis of the road network. Moreover, evaluation of road …

This paper presents a comprehensive review of recent advancements in image processing
and deep learning techniques for pavement distress detection and classification, a critical …