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.

通过生成一个包含正常和有缺陷的合成纤维绳的综合数据集，本研究旨在支持自动化缺陷检测系统的开发，以优于传统的视觉检验方法，并在广泛应用中实现合成纤维绳的更安全、更高效的利用。