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.

中文翻译：

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集成增强现实和模式识别以进行裂纹检测的方法

现场目视检查具有与人类相关的固有挑战，例如准确性低、成本和时间过多以及安全性。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，研究人员提议使用增强现实 (AR) 将人类视觉检查与基于图像的自动裂纹检测相结合。然而，迄今为止，AR 裂缝检测是有限的，因为：（1）它不是实时可用的，（2）它需要外部处理设备。本文描述了一种新的 AR 方法，它解决了这两个问题，使独立的实时裂缝检测系统能够用于现场检查。将Canny算法转化为AR头显数字平台的一维数学环境。然后，该算法基于有限的耳机处理能力进行了简化，以缩短处理时间。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，具有实用的实时图像处理。