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

Bridge inspection and monitoring are usually used to evaluate the status and integrity of
bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods …

Predicting unsafe behaviour in advance can enable remedial measures to be put in place to
mitigate likely accidents on construction sites. Prevailing safety studies in construction tend …

This paper presents alternative solutions for classifying concrete spall severity based on
computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep …

The automatic recognition of cracks is an essential requirement for the cost‐efficient
maintenance of concrete structures, such as bridges, buildings, and roads. It should allow …

Unmanned aerial vehicles (UAVs), in conjunction with computer vision techniques, have
shown great potential for bridge inspections. Close-range images captured in proximity to …

The use of deep learning (DL) in civil inspection, especially in crack detection, has
increased over the past years to ensure long-term structural safety and integrity. To achieve …

Cracking in concrete structures affects performance and is a major durability problem.
Cracks must be detected and repaired in time in order to maintain the reliability and …

In recent years deep-learning techniques have been developed and applied to inspect
cracks in RC structures. The accuracy of these techniques leads to believe that they may …

This study investigates the semantic segmentation of common concrete defects when using
different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model …