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 …

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 …

The dataset is crucial for the results of crack segmentation in deep learning. However, the
quantity and quality of annotations in datasets used for crack segmentation are uneven, and …

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

A deep learning model named Cracklab for pixel level segmentation and measurement of
concrete cracks is proposed. Cracklab excels at handling cracks at image edges and …

Cracks can be important performance indicators for determining damage processes in new
and existing concrete structures. In recent years, deep convolutional neural networks …

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

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 …

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