Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

TL;DR

This paper evaluates Detectron2's Mask R-CNN for automated defect detection in synthetic fibre ropes, demonstrating its effectiveness in segmenting various damage types to improve inspection accuracy and safety.

Contribution

It introduces a novel application of Detectron2 with Mask R-CNN for defect segmentation in synthetic fibre ropes, utilizing a new dataset of high-dimensional images.

Findings

Detectron2 effectively segments seven damage classes in SFRs.

Mask R-CNN achieves high accuracy in defect detection.

Automated inspection improves safety and efficiency.

Abstract

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.