An Efficient Method to Extract the Axial Temperature Curve From Infrared Image of Porcelain Insulator String

The axial temperature is often used to reflect the condition of insulators in research on the insulator state. Traditional manual extraction methods are cumbersome. To improve research efficiency, this paper proposes a method for extracting insulator axis problems in order to address the challenge of obtaining a large number of insulator axis temperatures quickly in commercial software. The Retinaface network is simplified to develope a streamlined version called S-Retinaface, which facilitates training with infrared images and enables seamless industrial deployment. This method harnesses the S-Retinaface network, tailored for infrared data, ensuring precise detection of insulator keypoints. From these, the insulator's axis line equation can be computed, leading to the precise mapping of axial temperature profiles. Experimental validation on a test dataset demonstrates the accuracy of S-Retinaface, reflected by a high Percentage of Correct Keypoints Area Under Curve of 0.864, and showcases a commendable similarity with manual extraction methods, as evidenced by an average Dynamic Time Warping distance of 2.1008. This technique not only elevates the efficiency of temperature data collection but also promises an impact on the field's operational dynamics, offering a rapid, dependable means to assess insulator integrity.