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 (placking high, placking medium, placking low, compression, core out, chafing, 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.

翻译：采用最新技术的纤维绳因其轻质和高抗拉强度，已成为海上工业中替代钢绳的极具吸引力的选择。与此同时，对这些绳索进行频繁检查对于确保整个系统的正常运行和安全至关重要。深度学习模型在状态监测应用中的发展为合成纤维绳的缺陷检测提供了一种更简单、更有效的方法。本文研究了用于缺陷检测和实例分割的先进库Detectron2的性能。采用Mask R-CNN架构的Detectron2用于分割SFR中的缺陷。具有多种骨干网络配置的Mask R-CNN在一个通过实验获取的数据集上进行了训练和测试，该数据集包含1,803张高维图像，涵盖了SFR的七种损伤类别（分别为高股松散、中股松散、低股松散、压缩、芯线外露、磨损和正常）。通过利用Detectron2的强大功能，本研究旨在开发一种自动化且高效的SFR缺陷检测方法，以改进检查流程并确保纤维绳的安全。