This paper presents a novel algorithm for non-destructive damage detection
for steel ropes in high-altitude environments (aerial ropeway). The algorithm
comprises two key components: First, a segmentation model named RGBD-UNet is
designed to accurately extract steel ropes from complex backgrounds. This model
is equipped with the capability to process and combine color and depth
information through the proposed CMA module. Second, a detection model named
VovNetV3.5 is developed to differentiate between normal and abnormal steel
ropes. It integrates the VovNet architecture with a DBB module to enhance
performance. Besides, a novel background augmentation method is proposed to
enhance the generalization ability of the segmentation model. Datasets
containing images of steel ropes in different scenarios are created for the
training and testing of both the segmentation and detection models. Experiments
demonstrate a significant improvement over baseline models. On the proposed
dataset, the highest accuracy achieved by the detection model reached 0.975,
and the maximum F-measure achieved by the segmentation model reached 0.948.

本文提出了一种新颖的算法用于高海拔环境（空中缆车）中钢丝绳的无损损伤检测，该算法包括两个主要组成部分：首先，设计了一个名为 RGBD-UNet 的分割模型，能够准确地从复杂背景中提取钢丝绳。该模型通过提出的 CMA 模块能够处理和组合颜色和深度信息。其次，开发了一个名为 VovNetV3.5 的检测模型，用于区分正常和异常的钢丝绳。它将 VovNet 架构与 DBB 模块集成以提高性能。此外，提出了一种新颖的背景增强方法以增强分割模型的泛化能力。已创建包含不同场景下钢丝绳图像的数据集，用于训练和测试分割和检测模型。实验证明与基准模型相比有显著改进。在提出的数据集上，检测模型达到了最高 0.975 的准确率，分割模型达到了最高 0.948 的 F-measure 值。

一种光学钢绳无损损伤检测的新方法

A new method for optical steel rope non-destructive damage detection

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。