The architecture, engineering and construction (AEC) industry is experiencing a
technological revolution driven by booming digitisation and automation. Advances in …

With the advancement of machine learning (ML) and deep learning (DL), there is a great
opportunity to enhance the development of automatic crack detection algorithms. In this …

Previous research has shown the high accuracy of convolutional neural networks (CNNs) in
asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation …

Computer vision techniques, in conjunction with acquisition through remote cameras and
unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure …

This paper compares the performance of common edge detectors and deep convolutional
neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of …

Crack is an important indicator for evaluating the damage level of concrete structures.
However, traditional crack detection algorithms have complex implementation and weak …

Clients are increasingly looking for fast and effective means to quickly and frequently survey
and communicate the condition of their buildings so that essential repairs and maintenance …

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of
artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains …

Visual inspection has been the most widely used technique for monitoring concrete
structures in service. Inspectors visually evaluate defects based on experience, skill, and …

This paper presents a vision-based crack detection approach for concrete bridge decks
using an integrated one-dimensional convolutional neural network (1D-CNN) and long short …