Methodology to integrate augmented reality and pattern recognition for crack detection
Methodology to integrate augmented reality and pattern recognition for crack detection
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DOI：
10.1111/mice.12932
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Kaveh Malek;Ali Mohammadkhorasani;F. Moreu
中科院分区：
文献类型：
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作者：
Kaveh Malek;Ali Mohammadkhorasani;F. Moreu
现场目视检查具有与人类相关的固有挑战，例如精度低、成本和时间过高以及安全性。为了克服这些障碍，研究人员和行业领导者开发了基于图像的自动结构裂缝检测方法。最近，研究人员提出使用增强现实（AR）将人类视觉检查与基于图像的自动裂缝检测相结合。然而，迄今为止，AR裂缝检测是有限的，因为：（1）它不能在真实的时间内获得，以及（2）它需要外部处理设备。本文介绍了一种新的AR方法，解决了这两个问题，使一个独立的真实的实时裂纹检测系统的现场检查。Canny算法被转换到AR耳机数字平台的一维数学环境中。然后，该算法被简化的基础上有限的耳机处理能力，朝着更低的处理时间。AR裂纹检测方法的测试消除了AR图像处理对外部处理器的依赖，具有实用的真实的实时图像处理。
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.