Insulator defect detection based on feature refining network
JIANG Xiangju
WANG Ruitong
MA Yanhong
Abstract：Regular inspection of insulator conditions is a crucial part for ensuring the safe operation of power grids.To address the challenges of unsatisfactory insulator defect detection performance in aerial images,caused by defect regions occupying a small proportion of the image and target sizes vary-ing inconsistently,a defect detection algorithm for insulators based on a feature refinement network is proposed.First,the algorithm uses the Focal Modulation Network(FocalNet)to encode spatial con-text at multiple granularity levels and integrates it with Spatial Pyramid Pooling Faster Cross Stage Partial Channel(SPPFCSPC)to construct the feature extraction backbone,enhancing the network's feature extraction capabilities.Next,an enhanced feature-adaptive fusion pyramid is designed,incor-porating a positioning information supplementation branch to mitigate the loss of defect features.Addi-tionally,Efficient Multi-Scale Attention(EMA)is introduced to generate rich semantic feature maps at different resolutions.Finally,the Feature Refinement Detection Head extracts and aggregates multi-scale feature information of insulators and defects,producing more discriminative features for detecting targets of varying scales.Experimental results demonstrate that proposed method achieves an mAP of 98.2％,effectively identifying multi-scale insulators and defects,thereby providing references for multi-scale detection in insulator aerial images.
Keywords：insulator defect detectionintelligent inspectionmulti-scale target detectionfeature refinementdeep learning
Publication Date：2025-06-30
Online Publishing Date：2025-08-21(First online date of this platform, not the publication date of the document)
Pages：11( 79-89 )