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.

翻译：本文提出了一种用于高空环境（空中索道）中钢索无损损伤检测的新算法。该算法包含两个关键组成部分：首先，设计了一个名为RGBD-UNet的分割模型，用于从复杂背景中精确提取钢索。该模型具备通过所提出的CMA模块处理和融合颜色与深度信息的能力。其次，开发了一个名为VovNetV3.5的检测模型，用于区分正常与异常钢索。该模型将VovNet架构与DBB模块相结合以提升性能。此外，提出了一种新的背景增强方法，以提升分割模型的泛化能力。针对不同场景下的钢索图像构建了数据集，用于分割模型和检测模型的训练与测试。实验表明，该算法较基准模型有显著改进。在所提数据集上，检测模型的最高准确率达到0.975，分割模型的最大F值达到0.948。