计算机视觉和增强现实技术用于以人为本的疲劳裂纹检测

Rushil Mojidra, Jian Li, Ali Mohammadkhorasani, Fernando Moreu, C. Bennett, William N. Collins
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引用次数: 0

摘要

美国有相当一部分桥梁的使用寿命已超过 50 年的设计寿命,其中许多桥梁的状况很差,很容易出现疲劳裂缝,从而导致灾难性的故障。然而,目前基于人工视觉的疲劳裂缝检测方法费时费力,而且容易出错。我们提出了一种新颖的以人为本的桥梁检测方法,通过采用计算机视觉和增强现实(AR)等先进技术来提高疲劳裂缝检测的效率和准确性。特别是,我们开发了一种基于计算机视觉的算法,通过分析 AR 头显移动摄像头记录的短视频中的结构表面运动,实现近乎实时的疲劳裂缝检测。该方法通过跟踪特征点和测量特征点对之间的距离变化来监测结构表面,从而识别与裂缝开合相关的运动模式。与改进前的位移变化相比,测量特征点之间的距离变化无需对摄像头进行运动补偿,因此可以利用非稳态 AR 头显进行可靠且计算效率高的疲劳裂纹检测。此外,还创建了一个 AR 环境,并与计算机视觉算法集成。裂缝检测结果被传输到桥梁检测人员佩戴的 AR 头显,在那里被转换成全息图,并固定在三维真实世界环境中的桥梁表面上。AR 环境还提供虚拟菜单,支持人在回路中决策,以确定最佳裂缝检测参数。这种以人为本的方法改进了可视化和人机协作,有助于检测人员在现场以接近实时的方式做出明智的决策。针对平面内和平面外疲劳裂纹,使用两个实验室测试装置对所提出的裂纹检测方法进行了全面评估。最后,利用集成的 AR 环境,进行了一次以人为中心的桥梁检测,以证明所提方法的功效和潜力。
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Computer Vision and Augmented Reality for Human-Centered Fatigue Crack Inspection
A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human–machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology.
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