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Automatic visual inspection of synthetic fibre ropes (SFRs) is a challengingtask in the field of offshore, wind turbine industries, etc. The presence ofany defect in SFRs can compromise their structural integrity and posesignificant safety risks. Due to the large size and weight of these ropes, itis often impractical to detach and inspect them frequently. Therefore, there isa critical need to develop efficient defect detection methods to assess theirremaining useful life (RUL). To address this challenge, a comprehensive datasethas been generated, comprising a total of 6,942 raw images representing bothnormal and defective SFRs. The dataset encompasses a wide array of defectscenarios which may occur throughout their operational lifespan, including butnot limited to placking defects, cut strands, chafings, compressions, core outsand normal. This dataset serves as a resource to support computer visionapplications, including object detection, classification, and segmentation,aimed at detecting and analyzing defects in SFRs. The availability of thisdataset will facilitate the development and evaluation of robust defectdetection algorithms. The aim of generating this dataset is to assist in thedevelopment of automated defect detection systems that outperform traditionalvisual inspection methods, thereby paving the way for safer and more efficientutilization of SFRs across a wide range of applications.

Imagery Dataset for Condition Monitoring of Synthetic Fibre Ropes