A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50

Abstract

The identification of stay cable icing is crucial for robot deicing to improve efficiency and prevent damage to stay cables. Therefore, it is significant to identify the areas and degree of icing in the images of stay cables. This study proposed a two-stage model that combines U-Net and ResNet50. In the first stage, this model used U-Net to segment the surface ice and icicles from the stay cable. The image of icing obtained after segmentation was used as the input for the second stage. In the second stage, ResNet50 was used to classify the degree of icing. The experimental results show that the proposed model can successfully segment the icicles and surface ice from the stay cable icing image to complete the classification of the icing degree. The mean pixel accuracy and intersection over the union of icing were 96.65% and 82.10%, respectively. The average accuracy of the icing degree classification was 95.71%. The method proposed in this study meets the requirements of robustness, segmentation accuracy, and classification accuracy for stay cable icing recognition, which provides a research basis for the precise icing recognition of cable-deicing robots.