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.
StatusUdgivet - okt. 2023


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