Automatic visual inspection of synthetic fibre ropes (SFRs) is a challenging task in the field of offshore, wind turbine industries, etc. The presence of any defect in SFRs can compromise their structural integrity and pose significant safety risks. Due to the large size and weight of these ropes, it is often impractical to detach and inspect them frequently. Therefore, there is a critical need to develop efficient defect detection methods to assess their remaining useful life (RUL). To address this challenge, a comprehensive dataset has been generated, comprising a total of 6,942 raw images representing both normal and defective SFRs. The dataset encompasses a wide array of defect scenarios which may occur throughout their operational lifespan, including but not limited to placking defects, cut strands, chafings, compressions, core outs and normal. This dataset serves as a resource to support computer vision applications, including object detection, classification, and segmentation, aimed at detecting and analyzing defects in SFRs. The availability of this dataset will facilitate the development and evaluation of robust defect detection algorithms. The aim of generating this dataset is to assist in the development of automated defect detection systems that outperform traditional visual inspection methods, thereby paving the way for safer and more efficient utilization of SFRs across a wide range of applications.

翻译：在海上风电等行业中，合成纤维绳索（SFRs）的自动视觉检测是一项具有挑战性的任务。绳索中的任何缺陷都可能损害其结构完整性并构成重大安全风险。由于这些绳索的尺寸和重量较大，频繁拆卸和检查往往不切实际。因此，亟需开发高效的缺陷检测方法以评估其剩余使用寿命（RUL）。为应对这一挑战，我们生成了一套全面的数据集，包含6,942张原始图像，涵盖正常和缺陷状态的合成纤维绳索。该数据集广泛涵盖了绳索全生命周期中可能出现的各类缺陷场景，包括但不限于编绳缺陷、断股、磨损、挤压、芯线外露及正常状态。该数据集可作为支撑计算机视觉应用的资源，涵盖目标检测、分类与分割等任务，旨在检测和分析合成纤维绳索缺陷。本数据集的公开将促进鲁棒缺陷检测算法的开发与评估。生成该数据集的目标是协助开发自动化缺陷检测系统，使其性能超越传统视觉检测方法，从而为合成纤维绳索在各类应用中的更安全、更高效使用铺平道路。