## 5 Conclusions  
To address the low precision issue of existing detection models for ice-covered insulator detection, this paper proposes the LDKA-NET defect detection algorithm based on the wide field of view dynamic convolution network. The main contributions are as follows:  
Figure 11: Transmission lines ice cover interface design diagram.  
Figure 10: PR curve analysis and normalized confusion matrix display.  
(1) The wide field of view convolution network (WFVC Net) employs larger convolutional kernels to enhance the network's perceptual and generalization capabilities. It achieves a larger effective receptive field with fewer layers while also exhibiting higher shape bias, enabling it to better capture shape information in images.  
(2) The full-dimensional dynamic convolution feature fusion network, as compared to traditional static convolution, significantly improves CNN accuracy by learning linear combinations of multiple convolutional kernels and the weighted attention related to their inputs. This approach offers higher parameter efficiency and enhanced feature extraction capabilities.  
(3) The Expectation Maximization Dynamic Convolution Attention (EM-DCA) mechanism focuses on and utilizes important information within the input data, aiding the model in better allocating attention to significant targets or scene components. This results in learning more generalized feature representations.  
Results indicate that the proposed algorithm achieves an overall average precision of 99.01%, outperforming comparative algorithms. Future work will focus on enhancing the lightweight universality of this method, deploying lightweight models on the edge to realize integrated applications at the cloud-edge terminal.  
Aiming at the problem of insufficient accuracy of existing detection models in the detection of ice-covered insulators, this study proposed the LDKA-NET algorithm and achieved a significant improvement in the average detection accuracy of 99.01%, breaking through the bottleneck of traditional algorithms in terms of the limitation of sensing field and insufficient feature extraction ability, and fully embodying the practical value. By using the full-dimensional dynamic convolution feature fusion network and the expected maximum dynamic convolution attention mechanism, the algorithm achieves high parameter efficiency, a low computation cost, and a stronger feature generalization ability, and is suitable for ice cover defect detection in a variety of complex scenes. In addition, its potential in cloud-edge-to-end lightweight deployment further improves the scalability of practical engineering applications, and is of great significance for real-time monitoring and fault location of power inspection systems.  
Mhtodology, X.D.; formal analysis, C.J.; writing--original draft preparation, X.D.; Writing--Reviewing and editing, Y.Z.; Supervise, X.D.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the research project of State Grid Xinjiang Electric Power Co., Ltd. (Program No. 5230 DK240003).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Author [PERSON] and [PERSON] were employed by the company State Grid Xinjiang Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.