A deep learning-based method for extracting ice cover on power transmission lines

With the continuous development and expansion of power systems, the safe operation of transmission lines has become particularly crucial. In response to the shortcomings of traditional methods, which require manual adjustment of parameters and features, this paper proposes a deep learning-based line ice extraction algorithm, IDR-Net. IDR-Net includes a multiscale ice feature extraction module that can extract features of ice at various scales on the line, facilitating the extraction of features from ice-covered targets of different sizes. IDR-Net's DualUP tandem dual upsampling module can prevent the checkerboard effect commonly associated with traditional upsampling modules, thus enhancing the capability of feature extraction and the precision of image edge segmentation during upsampling. Additionally, IDR-Net's residual ice feature extraction module helps avoid the issues of gradient explosion and disappearance, allowing the network to retain more feature information. After experimenting on a self-constructed dataset, the results show that the improved network achieved a mean Intersection over Union (mIoU) of 81.55%, an increase of 4.13 percentage points, demonstrating that the improved network can effectively enhance the accuracy of remote sensing image segmentation.