Vision-based inspection of out-of-plane fatigue cracks in steel structures

Mojidra, Rushil; Li, Jian; Mohammadkhorasani, Ali; Moreu, Fernando; Collins, William E.; Bennett, Caroline; Taher, Sdiq Anwar

doi:10.1117/12.2613188

Cited by 1 publication

(2 citation statements)

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“…The approach with an external processor, however, involves non-real-time processing because of the latency that exists in the connection establishment between the AR device and the processing unit (L. Liu et al, 2019). For example, Mojidra et al (2022) developed a system for crack detection in structures using AR. They created a computer vision algorithm to analyze short videos recorded from cracked surfaces and made a database and hotspot connection between the AR headset and the algorithm.…”

Section: Ar Standalone Crack Detectionmentioning

confidence: 99%

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Methodology to integrate augmented reality and pattern recognition for crack detection

Malek

Mohammadkhorasani

Moreu

2022

Computer aided Civil Eng

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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.

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Section: Ar Standalone Crack Detectionmentioning

confidence: 99%

“…For example, Mojidra et al. (2022) developed a system for crack detection in structures using AR. They created a computer vision algorithm to analyze short videos recorded from cracked surfaces and made a database and hotspot connection between the AR headset and the algorithm.…”

Section: Proposed Ar‐crack Detectionmentioning

confidence: 99%