Accurate Detection and Classification of Surface Defects in Electric Porcelain Insulators Based on Deep Learning Intelligent Algorithms

The traditional surface defect detection method of porcelain insulators has obvious shortcomings in accuracy. This paper introduces an intelligent algorithm based on deep learning to achieve accurate detection and classification of surface defects of porcelain insulators. First, a high-resolution image acquisition system is used to comprehensively scan the surface of porcelain insulators to obtain high-quality original image data. Then, the original data is processed based on data enhancement technology to generate diversified training samples to improve the generalization ability of the model. Then, a deep learning model that integrates YOLOv5 (You Only Look Once Version 5) and ResNet50 (Residual Networks 50) is designed. The pre-training weights are optimized through transfer learning, which improves the recognition effect of the model on complex defect types. Finally, in order to further improve the detection accuracy, multi-scale detection and feature fusion technology are used to solve the problems in small-size defects and large-scale image data processing. The deep learning model proposed in this study has an accuracy of 91.50%, a recall of 89.00%, a precision of 90.20%, and an F1-score of 89.60% in the detection and classification of defects in porcelain insulators without using data enhancement. Finally, the triple data enhancement combination of rotation, cropping, and brightness adjustment further improves the performance of the model, with an accuracy of 94.30%, a recall of 92.50%, and a precision of 93.20%, respectively, and an F1-score of 92.80%. This method not only has high accuracy and robustness but also can achieve efficient and automated defect detection in actual industrial applications, providing a strong guarantee for the safe operation of the power system.