Supervised and semi-supervised semantic segmentation algorithms require significant
amount of annotated data to achieve a good performance. In many situations, the data is …

Deep Learning (DL) semantic image segmentation is a technique used in several fields of
research. The present paper analyses semantic crack segmentation as a case study to …

Surface crack segmentation poses a challenging computer vision task as background,
shape, color and size of cracks vary. In this work we propose optimized deep encoder …

A large crack detection dataset of 2446 manually labeled images is established to cover a
wide range of noise and to evaluate the performance of end‐to‐end deep convolutional …

As surface crack detection is essential for roads and other building structures in most
countries, this has been a very popular topic in computer vision for automating structural …

The detection of surface crack is essential to ensure the safety and the serviceability of civil
infrastructure. The automatic method is highly efficient and the test results are objective …

Obtaining spatial and topological information for cracks in construction materials is important
for the evaluation of service performance in infrastructure engineering. Manually extracting …

Structures suffer from the emergence of cracks, therefore, crack detection is always an issue
with much concern in structural health monitoring. Along with the rapid progress of deep …

Crack detection is a critical task in routine inspection of building structures. Most of the
traditional crack detection methodologies are conducted by human inspectors that may …

Automatic crack detection from images is a challenging problem long studied by many, and
it has a potential to drastically reduce the labor intensive building and road inspections …