Artificial intelligence-assisted intelligent detection model for microscopic defects in ultra-high voltage shielding clothing materials

Ultra-high voltage (UHV) transmission systems require shielding clothing with high conductivity, uniform structure, and stable electromagnetic protection to ensure operator safety. Microscopic defects like pinholes, micro-cracks, fiber fractures, and coating discontinuities severely reduce effectiveness and are difficult to detect with conventional manual inspection. This suggested model proves to be an efficient, energy-aware, reliable, and scalable solution for the intelligent quality assessment of the UHV shielding clothing manufacturing and maintenance processes. In particular, the AI-aided Intelligent Detection Model is created to automatize and improve the accuracy of classification of microscopic fabric defects, which can be used to detect fabric defects of UHV shielding clothing materials. For this experiment, the Kaggle Multi-Class Fabric Defect Detection Dataset has been used as a source, it includes 3077 images of microscopic fabric defects belonging to several classes of defects. These images belong to generic images of fabric defects and do not contain any information about UHV shielding clothing, conductive threads, or EM shielding deterioration. In this regard, the following results have been achieved with the accuracy (98.42%), precision (96.81%), recall (95.37%), F1-score (96.08%), and computational efficiency (97%). It proves to be an efficient tool to classify microscopic fabric defects, while further research can be dedicated to the application of this method to the classification of fabric defects of UHV shielding clothes.