In this paper, artificial intelligence (AI) will be applied for the first time in the context of glass
processing. The goal is to use an algorithm based on artificial intelligence to detect the …

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 …

Obtaining extensive, high-quality datasets for crack segmentation with pixel-level labels is
expensive and labor-intensive. The Unified Weakly and Semi-supervised Crack …

Cracks are the defects formed by cyclic loading, fatigue, shrinkage, creep, and so on. In
addition, they represent the deterioration of the structures over some time. Therefore, it is …

Through extensive research on deep learning in recent years and its application in
construction, crack detection has evolved rapidly from rough detection at the image-level …

Semantic segmentation using convolutional neural networks (CNN) is a crucial component
in image analysis. Training a CNN to perform semantic segmentation requires a large …

Surface defect detection is an extremely challenging task. Surface defects typically exhibit
weak appearances and complex boundaries. Some detection methods are limited by their …

Infrastructures are omnipresent today, with some structures weakening over the years due to
natural disasters and aging. Periodic structural health monitoring is essential to keep the …

Detecting cracks from images using embedded deep learning applications requires efficient
and lightweight models in practice. To improve the computational efficiency of models, it is …

Inspection for cracks is an essential yet labor-intensive aspect of maintenance for structures
in active service bridges. Deep learning networks, combined with an abundance of …