Methodology to integrate augmented reality and pattern recognition for crack detection

Abstract

In-field visual inspections have inherent challenges associated with humans such as low accuracy, excessive cost and time, and safety. To overcome these barriers, researchers and industry leaders have developed image-based methods for automatic structural crack detection. More recently, researchers have proposed using augmented reality (AR) to interface human visual inspection with automatic image-based crack detection. However, to date, AR crack detection is limited because: (1) it is not available in real time and (2) it requires an external processing device. This paper describes a new AR methodology that addresses both problems enabling a standalone real-time crack detection system for field inspection. A Canny algorithm is transformed into the single-dimensional mathematical environment of the AR headset digital platform. Then, the algorithm is simplified based on the limited headset processing capacity toward lower processing time. The test of the AR crack-detection method eliminates AR image-processing dependence on external processors and has practical real-time image-processing.

Augmented reality‐based method for road maintenance operators in human–robot collaborative interventions

“…Tarek and Marzouk (2021) propose a handheld mobile AR application for the visualization of all inspection data using building information modeling (BIM) technologies during infrastructure maintenance activities. Ren et al (2022) and Malek et al (2023) propose a method for a real-time crack detection system with an AR interface, in real-time, and with no need for an external processing device. The authors in Sadhu et al (2023) utilize in their research both AR and VR in combination with BIM technologies for structural health monitoring.…”

“…(2022) and Malek et al. (2023) propose a method for a real‐time crack detection system with an AR interface, in real‐time, and with no need for an external processing device. The authors in Sadhu et al.…”

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

“…Specifically, DL‐based CV methods hold the potential as a technological solution for implementing automated measurements in the construction field. DL‐based CV methods have been applied in the construction field, and in recent years, researchers have reported numerous achievements regarding automatic detection or segmentation of defects or cracks in construction components (Ge et al., 2023; Guo et al., 2021; Jiang & Zhang, 2020; Li et al., 2018; Malek et al., 2022; Mirzazade et al., 2023; Pan & Zhang, 2022; Yeum & Dyke, 2015). Despite the progress made, there are still challenges to overcome in implementing automated measurement in the construction field.…”

“…On a positive note, DL‐based CV methods are progressively addressing object detection (Liang, 2019; Malek et al., 2022) and segmentation (Pan & Zhang, 2022) challenges in the construction fields. The problem of converting photos to sectional drawings can also be technically feasible through the application of DL‐based image‐to‐image transformation models (Chaillou, 2020; Liu & Xu, 2022).…”

“…As a result, it is not easily applied in fields with limited training datasets that are relatively difficult to obtain (Liu & Xu, 2022), such as the construction field. Consequently, most research on CV methods in the construction field has focused on crack detection (Ge et al., 2023; Jiang & Zhang, 2020; Li et al., 2018; Malek et al., 2022; Mirzazade et al., 2023; Yeum & Dyke, 2015), for which datasets are comparatively easier to acquire.…”