Research on Detection of Icing Cover Transmission Lines Under Different Weather Conditions Based on Wide-Field Dynamic Convolutional Network LDKA-NET

The safety of transmission lines is a crucial guarantee for the operation of the power grid. To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). First, a wide field of view convolutional network (WFVC Net) is introduced to enhance the network’s perception and generalization capabilities, enabling better adaptation to complex scene targets. Secondly, a full-dimensional dynamic convolutional feature fusion network is proposed, which strengthens the model’s feature extraction ability by learning linear combinations of multiple convolution kernels and their weighted input-related attention. Finally, an Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is introduced, which focuses on and utilizes important information in the input data to help the model better allocate attention, thereby improving its generalization and robustness. Experimental results show that on the dataset proposed in this paper, the average accuracy of the improved algorithm ([email protected]) reaches 99.01%. The model parameters decreased by 47.7 M compared with the baseline model and 91.26 M compared with the Faster region with CNN feature (Faster R-CNN) model. The accuracy is 4.81% and 3.11% higher than the SSD model and YOLOv5-L model, respectively. Compared with the existing models, our model is smaller and has higher detection accuracy. It can accurately detect ice-covered wires and complete the task of the ice-covered detection of transmission lines.