Civil infrastructure (eg, buildings, roads, underground tunnels) could lose its expected
physical and functional conditions after years of operation. Timely and accurate inspection …

Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …

Structural health monitoring at local and global levels using computer vision technologies
has gained much attention in the structural health monitoring community in research and …

Accurate pavement surface crack detection is essential for pavement assessment and
maintenance. This study aims to improve pavement crack detection under noisy conditions …

Cracking is a common pavement distress that would cause further severe problems if not
repaired timely, which means that it is important to accurately extract the information of …

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 …

Pavement crack detection is a critical task for insuring road safety. Manual crack detection is
extremely time-consuming. Therefore, an automatic road crack detection method is required …

Annually, millions of dollars are spent to carry out defect detection in key infrastructure
including roads, bridges, and buildings. The aftermath of natural disasters like floods and …

Automatic crack detection from images of various scenes is a useful and challenging task in
practice. In this paper, we propose a deep hierarchical convolutional neural network (CNN) …

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