论文标题
一种自动化系统，用于检测风力涡轮机叶片的视觉损害
An Automated System for Detecting Visual Damages of Wind Turbine Blades
论文作者
论文摘要
风能在市场水平上与化石燃料竞争的能力取决于降低风的高运营成本。由于风力涡轮机叶片的损坏是造成这些操作问题的主要原因，因此确定刀片损坏至关重要。但是，最近在视觉识别刀片损坏方面的著作仍在实验中，并专注于优化传统的机器学习指标，例如IOU。在本文中，我们认为在实现“最佳”模型性能之前将模型推向生产很久仍然可以为此用例产生实际价值。我们讨论了损害在生产中的建议模型的性能以及该系统如何与人类协调，作为商业化产品的一部分，以及它如何有助于降低风能的运营成本。
Wind energy's ability to compete with fossil fuels on a market level depends on lowering wind's high operational costs. Since damages on wind turbine blades are the leading cause for these operational problems, identifying blade damages is critical. However, recent works in visual identification of blade damages are still experimental and focus on optimizing the traditional machine learning metrics such as IoU. In this paper, we argue that pushing models to production long before achieving the "optimal" model performance can still generate real value for this use case. We discuss the performance of our damage's suggestion model in production and how this system works in coordination with humans as part of a commercialized product and how it can contribute towards lowering wind energy's operational costs.
