面向风力涡轮机可持续性的微可见表面裂纹检测
Barely-Visible Surface Crack Detection for Wind Turbine Sustainability
AI总结：
针对现有风力涡轮机缺陷检测算法难识别微可见发丝裂纹、相关数据集不足且地理多样性差的问题，本文提出全新的多样性微可见发丝裂纹数据集，并介绍了从图像采集到自动生成维护建议的端到端检测流水线，助力提升风电运维效率。
摘要：
风能生产是可持续发展以及降低化石燃料依赖的关键环节。为保障风能生产而维持风力涡轮机的结构完整性是一项成本高、耗时长的工作，需要反复进行检查与维护。虽然自主无人机已被证实能提升这一流程的效率，但用于检测异常以避免涡轮叶片遭受灾难性损坏的算法仍存在滞后——原因在于部分危险缺陷（如发丝裂纹）属于微可见缺陷。现有数据集和相关研究存在不足，多倾向于检测明显可见的缺陷，且地理多样性不足。本文提出了一个全新的、具备多样性的微可见发丝裂纹数据集，该数据集采集自多次风力涡轮机检查工作。为验证数据集的有效性，我们详细介绍了已部署的端到端涡轮裂纹检测流水线，覆盖从图像采集阶段到利用检测预测结果自动生成维护建议的全流程，旨在延长风力涡轮机的使用寿命并提升其运行效率。
英文摘要：
The production of wind energy is a crucial part of sustainable development and reducing the reliance on fossil fuels. Maintaining the integrity of wind turbines to produce this energy is a costly and time-consuming task requiring repeated inspection and maintenance. While autonomous drones have proven to make this process more efficient, the algorithms for detecting anomalies to prevent catastrophic damage to turbine blades have fallen behind due to some dangerous defects, such as hairline cracks, being barely-visible. Existing datasets and literature are lacking and tend towards detecting obvious and visible defects in addition to not being geographically diverse. In this paper we introduce a novel and diverse dataset of barely-visible hairline cracks collected from numerous wind turbine inspections. To prove the efficacy of our dataset, we detail our end-to-end deployed turbine crack detection pipeline from the image acquisition stage to the use of predictions in providing automated maintenance recommendations to extend the life and efficiency of wind turbines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
