Transformer Core Defect Detection Algorithm Based on Improved YOLOv8
HU Weihao
Abstract：To address the problem that transformer core defects pose a potential risk to the safe and stable operation of power grid systems but lack of detection means,a transformer core defect detection algorithm based on improved you only look once version 8(YOLOv8)was proposed.The algorithm firstly reduced the number of model parameters by introducing a ghost network(GhostNet),while maintaining a high detection accuracy.Secondly,it introduced a small-target detection layer with a large-size feature map,which was suitable for the detection of small-object.Finally,a dynamic detection head(DyHead)incorporating an attention mechanism was introduced to enhance the characterization of the detection head.After training and testing on the magnetic core dataset,the results showed that the algorithm's mean average accuracy rate was improved by 4.3％compared with the original YOLOv8 algorithm,and the amount of model parameters was reduced by 51.8％,which achieved a high accuracy rate and also met the deployment requirements of edge computing devices.The algorithm can detect transformer core defects more accurately and provide important technical support for the improvement of power grid safety performance.
Keywords：magnetic defect detectionGhostNetDyHeadsmall objectYOLOv8
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 539-543 )