Towards Remote Inspections of FPSO's Using Drones Instrumented with Computer Vision and Hyperspectral Imaging

E. Stensrud, Are Torstensen, D. Lillestøl, Kristian Klausen
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引用次数: 3

Abstract

The Class Society DNV has performed production surveys in enclosed spaces using drones since 2016, demonstrating cost savings and increased personnel safety. The goal is to develop autonomous inspection drones to reduce the need to enter tanks and enable remote inspection. The vision is a drone that can fly by itself, track where it is, and spot rust and cracks, and measure steel thickness. We expect that drone-assisted remote inspection will reduce survey costs for the clients and be a major safety improvement for surveyors. Several drone capabilities are required to enable visual close-up inspection and non-destructive testing in enclosed, GPS-denied, and poorly lit environments. In this study, we report the most recent status from an ongoing research project, including several industry partners. We highlight technical challenges and preliminary results on drone navigation functionalities, computer vision for detection of cracks, and the use of hyperspectral imaging to detect and classify the chemical composition of coatings, rust, and other use cases.
利用计算机视觉和高光谱成像技术的无人机对FPSO进行远程检测
自2016年以来,船级社DNV使用无人机在封闭空间进行生产调查,证明了成本节约和人员安全的提高。目标是开发自主检查无人机,以减少进入坦克的需要,并实现远程检查。这个构想是一架无人机,它可以自己飞行,跟踪自己的位置,发现生锈和裂缝,测量钢铁的厚度。我们期望无人机辅助远程检测将为客户降低测量成本,并对测量人员的安全进行重大改进。需要几种无人机功能,以便在封闭、gps屏蔽和光线不足的环境中进行视觉近距离检查和无损检测。在这项研究中,我们报告了一个正在进行的研究项目的最新状况,包括几个行业合作伙伴。我们强调了无人机导航功能的技术挑战和初步结果,用于检测裂缝的计算机视觉,以及使用高光谱成像来检测和分类涂层、铁锈和其他用例的化学成分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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