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Deep Learning for Critical Infrastructure Resilience

机译：深度学习以提高关键基础架构的弹性

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Ensuring the resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments. This paper intersects two unique fields-deep learning and critical infrastructure protection-and illustrates how deep learning can improve resiliency within the electricity sector. Machine vision is the combination of machine intelligence, or computer systems automatically learning patterns from exemplar data, and image analysis, or objects of interest being automatically segmented and identified from video image data. This technology has the potential to automate threat assessments in the context of securing critical infrastructures. Rather than traditional reactionary approaches, we present here a method of leveraging deep learning for the detection of threats to critical infrastructures before failures occur. This paper discusses the state-of-the-art in deep learning for creating machine vision systems, and the concepts are applied to increase the resiliency of critical infrastructures. The intersection between machine vision and critical infrastructures is discussed, as are key benefits and challenges of invoking such an approach, and examples within several fields of critical infrastructures are presented. Automated inspection of the power infrastructure using vehicle-mounted video acquisition equipment is explored, and a proof-of-concept implementation of a deep convolutional neural network is developed, achieving 95.5% accuracy in distinguishing power-related infrastructures within images largely typical of rural settings. These preliminary results show promise in the application of deep learning and machine vision to protecting critical infrastructures through preventative maintenance.
机译：确保关键基础设施的弹性在现代社会中至关重要，但是许多已部署的基础设施尚未充分利用现代技术发展。本文与深度学习和关键基础设施保护这两个独特的领域相交，并说明了深度学习如何提高电力部门的弹性。机器视觉是机器智能或计算机系统自动从示例数据中学习模式以及图像分析的组合，或者是从视频图像数据中自动分割和识别出感兴趣的对象。在确保关键基础架构安全的情况下，该技术具有自动进行威胁评估的潜力。与传统的反动方法不同，我们在这里提出一种利用深度学习在故障发生之前检测关键基础设施威胁的方法。本文讨论了用于创建机器视觉系统的深度学习领域的最新技术，并将这些概念应用于提高关键基础架构的弹性。讨论了机器视觉和关键基础设施之间的交叉点，以及调用这种方法的主要优点和挑战，并介绍了关键基础设施的多个领域中的示例。探索了使用车载视频采集设备对电力基础设施进行自动检查的方法，并开发了深度卷积神经网络的概念验证实现，在区分农村地区典型的图像中与电力相关的基础设施方面达到了95.5％的准确度。这些初步结果表明，在深度学习和机器视觉的应用中，有望通过预防性维护来保护关键基础架构。

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  • 来源
    《Journal of Infrastructure Systems》 |2019年第2期|05019003.1-05019003.11|共11页
  • 作者

    Dick Kevin; Russell Luke; Dosso Yasmina Souley; Kwamena Felix; Green James R.;

  • 作者单位

    Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada;

    Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada;

    Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada;

    Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada;

    Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada;

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