Insulator Micro-Defect Recognition Based on Improved YOLOv7 Model

In modern electric power systems, insulators, as a crucial component of transmission lines, have their condition directly linked to the safe and stable operation of the entire power grid. However, insulators are prone to minor defects under extreme weather and long-term operational conditions. These defects, if not detected and addressed in time, could evolve into serious safety hazards. Traditional insulator inspection methods rely on manual visual inspections or simple image processing techniques, which are not only inefficient but also struggle to meet the growing maintenance demands of high-voltage transmission lines in terms of accuracy and reliability. With the rapid development of deep learning technology, target detection models based on deep convolutional neural networks, such as the YOLO series, have demonstrated superior performance in many fields. This study aims to propose a more efficient and accurate method for detecting minor defects in insulators by improving the YOLOv7 model. The core of the research involves enhancing the model's ability to recognize minor defects against complex backgrounds through algorithm optimization, network structure adjustment, and improved training strategies. This paper will first review the research history and current status of insulator defect detection, analyzing the shortcomings of existing methods; then, it will detail the design and implementation of the proposed improved YOLOv7 model; followed by a series of experiments to verify the model's performance, comparing it with current advanced detection methods; and finally, discussing the practical application prospects and potential research expansion directions of the findings. The significance of this study lies not only in advancing insulator defect detection technology but also in providing new research ideas and technical support for the application of deep learning in other industrial fields.