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（架空索道）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。该模型通过所提出的CMA模块具备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，用于区分正常钢索与异常钢索。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，以提高分割模型的泛化能力。为训练和测试分割与检测模型，创建了包含不同场景下钢索图像的数据集。实验表明，该方法相较于基线模型有显著提升。在所提出数据集上，检测模型达到的最高准确率为0.975，分割模型达到的最大F值为0.948。