# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 5 Conclusions  
Given the diversity of insulator types, complex backgrounds, and the small size of faults, as well as the challenges faced by existing deep learning models in accurately diagnosing insulator faults. To improve upon the YOLOv8 baseline model, we propose the YOLOv8-DCP insulator fault diagnosis algorithm. The YOLOv8-DCP algorithm enhances the YOLOv8 baseline model by integrating the CW-DRB module, which improves the model's feature extraction and information fusion capabilities. Additionally, the introduction of the content-aware feature module CARAFE ensures that the reconstructed feature maps retain rich information, effectively addressing the issue of insufficient feature details during the up-sampling process. Finally, an extra small-fault detection layer is added to improve the model's precision in detecting small faults. Experimental results demonstrate that the YOLOv8-DCP model, incorporating the CW-DRB module, achieves improvements in [EMAIL_ADDRESS] compared to other C2f enhancement modules (C2f-ODConv, C2f-Faster, C2f-DySnakeConv), with increases of 1.2%, 1.8%, and 0.8% respectively. Ablation and comparative experiments show that YOLOv8-DCP exhibits overall performance enhancements over the baseline YOLOv8, with precision increased by 4.8%, recall improved by 0.1%, and [EMAIL_ADDRESS] elevated by 3.1%. Additionally, when compared to YOLOv5 and YOLOv7, YOLOv8-DCP demonstrates superior performance metrics, particularly with [EMAIL_ADDRESS] increasing by 3.0% and 4.3%, respectively. These results highlight the effectiveness and superiority of the YOLOv8-DCP algorithm. In addition, the preset functions of the fault diagnosis system were also successfully implemented. The YOLOv8-DCP algorithm integrates seamlessly with the diagnostic system, accurately identifying the types and quantities of faults within images, thereby confirming the practical applicability of the proposed algorithm. In summary, the improved algorithm proposed in this paper is effective and achieves higher accuracy in insulator fault diagnosis compared to other mainstream algorithms. This advancement is crucial for the intelligent maintenance of power grids and ensuring the safety of power transmission. It also contributes significantly to the intelligent inspection and maintenance of electrical infrastructure, enhancing the overall safety of power transmission systems. In the future, the research will focus on three main directions: the first direction is to expand the diversity of image data, ensuring that the dataset is sufficiently rich across different weather conditions and times of day, which will further enhance the model's generalization capability. The second direction is to focus on model lightweight, enabling the model to be quickly deployed on resource-constrained devices for real-time detection of insulator faults. The third direction is to develop a comprehensive fault diagnosis system that operates effectively in various complex and climatic conditions. Thisinvolves enhancing the model's ability to detect insulator defects, ensuring round-the-clock, real-time fault diagnosis. Conceptualization, W.J. and C.G. ; methodology, W.J. and C.G. ; software, C.G. and W.W.; validation, C.G. and H.L. ; formal analysis, C.G. and W.W.; investigation, C.G. ; resources, W.J. and H.L. ; data curation, C.G. ; writing--original draft preparation, C.G. ; writing--review and editing, W.J. ; visualization, C.G. ; supervision, W.J. and H.L. ; project administration, D.Z. ; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hunan Electric Power Co., Ltd. Project NO.5216 AJ21N005. Not applicable. The data presented in this study are available on request from the corresponding author due to privacy. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] is employee of State Grid Hunan Ultra-High Voltage Transmission Company.