The performance of crack segmentation is influenced by complex scenes, including
irregularly shaped cracks, complex image backgrounds, and limitations in acquiring global …

This paper reviews recent developments in deep learning-based crack segmentation
methods and investigates their performance under the impact from different image types …

Recently, crack segmentation studies have been investigated using deep convolutional
neural networks. However, significant deficiencies remain in the preparation of ground truth …

Accurate detection of crack defects in infrastructure is crucial to ensure their safety and
extend their service life. However, the presence of complex backgrounds, various shapes …

This article reports the development of a pure deep learning method for segmenting
concrete cracks in images. The objectives are to achieve the real-time performance while …

Detecting cracks are a great significance for the maintenance of the man-made buildings,
and deep learning methods such as semantic segmentation have greatly boosted this …

Routine visual inspection of concrete structures is essential to maintain safe conditions.
Therefore, studies of concrete crack segmentation using deep learning methods have been …

This research develops a novel computer vision approach named a spatial-channel
hierarchical network (SCHNet), which is feasible to support the automated and reliable …

Cracks in civil structures are important signs of structural degradation and may even indicate
the inception of catastrophic failure. Image‐based crack detection has been attempted in …

Cracks play a crucial role in assessing the safety and durability of manufactured buildings.
However, the long and sharp topological features and complex background of cracks make …