Amidst the unprecedented demographic boom, coupled with climate change, more pressure
is being exerted on road networks. Asset managers are thus in search for time and cost …

Environmental effects may lead to cracking, stiffness loss, brace damage, and other
damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) …

The detection of surface cracks or defects in roads, sewage pipes, pavements, shield
tunnels lining, etc. has become a promising area of research creating a significant impact on …

Automatic crack classification plays an essential role in road maintenance. Using many
features for the classification is inefficient for implementing embedded systems with low …

Existing deep learning (DL) models can detect wider or thicker segments of cracks that
occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow …

Surface crack detection is an integral part of infrastructure health surveys. This work
presents a transformative shift towards rapid and reliable data collection capabilities …

Existing crack recognition methods based on deep learning often face difficulties when
detecting cracks in complex scenes such as brake marks, water marks, and shadows. The …

Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual
inspection of cracks is time-consuming and prone to human error. Machine vision has been …

Crack detection on roads is essential nowadays because it has a significant impact on
ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more …

Crack detection or segmentation on concrete structures is a vital process in structural health
monitoring (SHM). Though supervised machine learning techniques have gained …