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.

翻译：风能生产是可持续发展和减少对化石燃料依赖的关键组成部分。维护风力涡轮机的完整性以生产这种能源是一项成本高昂且耗时的任务，需要反复检查和维护。虽然自主无人机已被证明能使这一过程更高效，但由于一些危险缺陷（如发丝裂纹）几乎不可见，用于检测异常以防止涡轮叶片灾难性损坏的算法却相对滞后。现有数据集和文献存在不足，且倾向于检测明显可见的缺陷，同时缺乏地理多样性。本文介绍了一个新颖且多样化的微不可见发丝裂纹数据集，该数据集收集自大量风力涡轮机检查。为证明我们数据集的有效性，我们详细阐述了从图像采集阶段到利用预测提供自动化维护建议的端到端部署涡轮裂纹检测流程，以延长风力涡轮机的寿命和效率。