Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet

The presence of ice on transmission lines threatens their integrity. The thickness of ice is crucial with respect to the autonomous operation of de-icing robots. Therefore, we propose a Cross-Guide-UNet (CG-UNet) model for detecting the thickness of ice on ice-covered transmission lines. The CG-UNet model uses an encoder–decoder architecture and incorporates a cross-guide module (CGM) at the crosslayer connection of the model. Initially, we redesign the attention calculation method to suit the shape characteristics of transmission lines. Channel and spatial attention are computed for the encoder and decoder features, respectively. Subsequently, the attention weight is cross-applied to the encoder and decoder features, allowing for comprehensive utilization of feature information to guide the generation of ice-covered edges. Additionally, we introduce a joint loss function comprising cross-entropy loss and boundary loss for model training, enhancing the distinction of easily confused edge pixels. The CG-UNet model effectively detects ice-covered edges through experimental analysis, achieving optimal dataset scale (ODS) and optimal image scale (OIS) scores of 0.934 and 0.938, respectively. The thickness detection error is constrained within 7.2%. These results confirm that the proposed algorithm can provide a control basis for the high-precision operation of de-icing robots.