## 1 Introduction
By designing the DFC-FasterNet module in the backbone feature extraction network, the receptive field is increased during the feature extraction process to enhance the network's detection accuracy, and the Neck module is redesigned for more accurate localization of insulator defect areas. However, the depth of this network model remains shallow, limiting its ability to extract features from images with particularly complex environmental backgrounds or occluded targets. Reference [9] proposes a hybrid model based on wavelet transform (WT), and Bat algorithm optimized Extreme Learning Machine (BA-ELM) was proposed in the literature. The noise reduction of ice cover thickness and meteorological factor data was carried out through wavelet transform, and segmentation processing was carried out to improve the prediction accuracy. Experimental results show that the accuracy of the model in ice thickness prediction is significantly improved. However, its limitations include a reliance on high-quality initial data, high computational complexity of the model, limited generalization ability, and insufficient ability to predict long-term dynamics. Reference [10] proposes a weakly supervised and phased transfer learning method based on YOLOv5 to identify insulators and different ice types, and improve recognition accuracy and anti-interference ability through multi-dimensional feature fusion. The insulator icing dataset was constructed based on the monitoring data of China Southern Power Grid from 2014 to 2018. The experimental results show that the model recognition accuracy is 86.6%, the recall rate is 91.3%, the speed is 8 ms/image, and the labeling efficiency is significantly improved by pseudo labeling. However, limitations include limited dataset time, inadequate adaptability to extreme environments, and the dependence of labeling intelligence on initial data quality. Reference [11] proposes a segmentation method based on non-subsampled contour wave transform (NSCT) and two -dimensional OSTU is proposed. The NSCT is used to denoise the image, and the improved genetic algorithm and two-dimensional OSTU algorithm are combined to achieve image segmentation. Experiments show that this method can effectively remove the noise of an ice-covered image, accurately extract the target, and reduce the search time, which is suitable for real-time application. However, its limitation is that the segmentation effect in a complex background needs to be verified, the computational complexity of the algorithm may limit its application in resource-constrained devices, and its adaptability to different types of noise needs to be further improved. [PERSON] et al. [12], in order to avoid the impact of black ice on traffic accidents, used the Mask-R-CNN model to detect black ice on the ground, compared different pre-trained models and architectures, analyzed the detected enveloping box data, and determined the degree of dangerous areas based on the number of divided pixels, which was verified by experiments. The detection accuracy reached 92.5%, which played a role in alerting passengers to black ice. However, the dataset of this study failed to fully reflect the diversity of the actual traffic environment, which may affect the generalization ability of the model. However, the limitation is that the time span of the dataset is limited, which may affect the generalization ability of the model, the robustness in complex environments needs to be verified, and the intelligent labeling is strongly dependent on the quality of the initial data. In conclusion, this paper proposes an ice-covered insulator defect identification method based on a wide-area visual dynamic convolutional network (LDKA-NET), which solves the problem of low target detection accuracy in complex environments.