  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.
