论文标题
KRAKN：基础设施维护中稀薄裂纹检测的转移学习框架
KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance
论文作者
论文摘要
监视基础设施的技术状况是维护其维护的关键要素。目前应用的方法已过时，劳动密集型和不准确。同时，由于两个主要因素（劳动密集型收集新数据集）和对计算能力的高需求，使用人工智能技术的最新方法在其应用中受到严重限制。我们建议利用自定义制造的框架-Krakn，以克服这些限制因素。它可以开发独特的基础设施缺陷在数字图像上的检测器，从而达到90％以上的准确性。该框架支持半自动创建新数据集并具有适度的计算能力要求。它是以公开分配给公众的现成软件包的形式实施的。因此，它可用于立即实施本文中政府部门基础设施管理过程中提出的方法，无论其财务能力如何。
Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence techniques are severely limited in their application due to two main factors -- labour-intensive gathering of new datasets and high demand for computing power. We propose to utilize custom made framework -- KrakN, to overcome these limiting factors. It enables the development of unique infrastructure defects detectors on digital images, achieving the accuracy of above 90%. The framework supports semi-automatic creation of new datasets and has modest computing power requirements. It is implemented in the form of a ready-to-use software package openly distributed to the public. Thus, it can be used to immediately implement the methods proposed in this paper in the process of infrastructure management by government units, regardless of their financial capabilities.
