Fibre ropes with the latest technology have emerged as an appealing
alternative to steel ropes for offshore industries due to their lightweight and
high tensile strength. At the same time, frequent inspection of these ropes is
essential to ensure the proper functioning and safety of the entire system. The
development of deep learning (DL) models in condition monitoring (CM)
applications offers a simpler and more effective approach for defect detection
in synthetic fibre ropes (SFRs). The present paper investigates the performance
of Detectron2, a state-of-the-art library for defect detection and instance
segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting
defects in SFRs. Mask R-CNN with various backbone configurations has been
trained and tested on an experimentally obtained dataset comprising 1,803
high-dimensional images containing seven damage classes (loop high, loop
medium, loop low, compression, core out, abrasion, and normal respectively) for
SFRs. By leveraging the capabilities of Detectron2, this study aims to develop
an automated and efficient method for detecting defects in SFRs, enhancing the
inspection process, and ensuring the safety of the fibre ropes.

利用 Detectron2 库中的 Mask R-CNN 架构，在合成纤维绳索中实现缺陷的自动检测，以提高检查过程并确保绳索的安全。