# 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. c 2024 14 3062  
will respond only when the icing thickness is large, and the icing state in front of the deicing robot cannot be reflected in real time. With the advancement in computer vision and image recognition technology, image recognition based on deep learning has been applied in various detection fields, such as face recognition [7], transmission line icing detection [8], and biomedical image analysis [9, 10]. Ref. [11] improved the accuracy of segmentation by introducing skip connections between encoder and decoder features, and this network is appropriately named U-Net and is used for segmenting neuron structures in the EM stack. Ref. [12] proposed the use of U-NET++ to evaluate pulmonary nodule segmentation, colonic polyp segmentation, nuclear segmentation, and liver segmentation. Ref. [13] proposed a hybrid deep convolutional neural network (CNN) model for the automatic and accurate prediction and segmentation of brain tumors from magnetic resonance imaging (MRI) images. These medical images have provided good segmentation results. Icing segmentation is similar to medical image segmentation; the pixels in the image are classified, so simple semantic segmentation is enough to deal with icing segmentation. However, semantic segmentation alone can only identify the icing area without providing information on the icing degree. This lack of accurate classification data of the icing degree makes it challenging to control the motor output effectively for deicing. On the one hand, excessive output will cause excessive mechanical wear and energy waste. On the other hand, insufficient output will affect the deicing effect. Therefore, automatic classification of icing severity has become an urgent need. Image classification is the key to achieving the classification of icing severity. With the emergence of image classification networks such as CNN [14], image classification based on deep learning has gradually become a new research topic; CNN has a mature application in the field of icing identification. [PERSON]. [15] proposed a new critical point-matching method based on the local multi-layer CNN features, termed Local Convolutional Features (LCFs), to measure the ice thickness of the PTLI. [PERSON]. [16] proposed an identification method for ice thickness based on a solid generalization convolution neural network (SGCNN), which is used to identify the thickness grade of the ice insulator of the transmission line. [PERSON]. [8] used the lightweight convolutional neural network MobileNetV3 for feature extraction and the multi-scale target detection network SSD to extract high latitude features to achieve ice thickness recognition. The deep learning model has the potential for icing detection, so the image classification network can be used to identify the icing degree of stay cables.