Verification, Validation and Processing of Infrastructure Drone Inspection Data

基础设施无人机检查数据的验证、验证和处理

• 批准号: 91977
• 金额: $ 24.23万
• 依托单位: PERCEPTUAL ROBOTICS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=91977
• 关键词: Verification; Validation; Processing; Infrastructure; Drone

Visual inspection of large infrastructure assets in extreme environments, such as offshore wind turbines, is essential for maintaining performance and ensuring safety. However, it is expensive, labour intensive, and hazardous, with the majority of inspections being carried out manually by inspectors using rope access or boom lift. Currently tens of millions of man hours per annum are spent looking for defects in safety critical infrastructure.Automatic visual inspections using robots equipped with cameras offers a potential solution, significantly reducing costs, increasing quality assurance and reducing safety concerns. Major progress has been made in capturing images for inspection, notably using drones, and in some cases, these have advanced to the point of being technically viable alternatives to traditional manual inspections.However, although the capture of images has been automated, the processing of images in order to identify defects remains a semi-automated process, with reliance on visual inspections of image data by trained experts. This means that although the visual data can be collected automatically and quickly, inspections still take a significant amount of time. Hence there is a need for fully automating the data processing component if these systems are to fully realise their potential.Alongside this there is also a need to garner wider acceptance of automatic processing techniques across the industry. They need to be commercially viable and demonstrably reliable - adoption of new approaches to inspection relies on a full understanding of their limitations and the criteria by which they can be assessed and categorised. This is necessary if such methods are to be acceptable to current and future regulatory requirements. Addressing this issue is therefore of equal importance to technical development.This project aims to address both of these issues. It is a collaboration between Garrad Hassan (DNV-GL), world leaders in risk management and quality assurance, Perceptual Robotics (PR), an SME specialising in visual inspection of wind turbines using drones, and the University of Bristol (UoB), experts in computer vision and AI. An automated processing pipeline will be developed and demonstrated, and incorporated within PRs Dhalian system, a world leading semi-automated drone inspection system for wind turbines. Alongside this, a general framework for assessing, characterising and comparing such systems will be validated and verified, with the aim of generating broader acceptance across the industry and informing future regulation. The project will provide both competitive advantage to PR and contribute to growth of the UK automated inspection industry.

在极端环境中对大型基础设施资产（如海上风力涡轮机）进行目视检查对于保持性能和确保安全至关重要。然而，这是昂贵的，劳动密集型的，并且危险的，大多数检查是由检查员使用绳索进入或吊杆升降机手动进行的。目前，每年有数千万工时用于寻找安全关键基础设施的缺陷。使用配备摄像头的机器人进行自动视觉检测提供了一种潜在的解决方案，可显著降低成本，提高质量保证并减少安全问题。在捕捉图像进行检测方面取得了重大进展，特别是使用无人机，在某些情况下，无人机已经发展到技术上可行的程度，可以替代传统的人工检测。然而，尽管图像捕捉已经实现自动化，但识别缺陷的图像处理仍然是一个半自动化的过程，依赖于训练有素的专家对图像数据的目视检查。这意味着虽然可以自动快速地收集视觉数据，但检查仍然需要大量的时间。因此，如果这些系统要充分发挥其潜力，就需要完全自动化数据处理组件。除此之外，还需要在整个行业中更广泛地接受自动处理技术。它们需要在商业上可行，并证明是可靠的-采用新的检查方法取决于对其局限性的充分理解以及对其进行评估和分类的标准。如果要使这些方法符合当前和未来的法规要求，这是必要的。因此，解决这一问题与技术发展同等重要，本项目旨在解决这两个问题。这是Garrad哈桑（DNV-GL），风险管理和质量保证领域的世界领导者，Perceptual Robotics（PR），一家专门从事使用无人机对风力涡轮机进行目视检查的中小企业，以及布里斯托大学（UoB），计算机视觉和人工智能专家之间的合作。将开发和演示自动化处理管道，并将其纳入PRs Dhalian系统，这是一个世界领先的风力涡轮机半自动无人机检测系统。与此同时，将验证和验证用于评估、表征和比较此类系统的总体框架，目的是在整个行业获得更广泛的接受，并为未来的监管提供信息。该项目将为PR提供竞争优势，并为英国自动检测行业的发展做出贡献。