电网技术2024，Vol.48Issue(2)：740-749,中插61-中插63,13.DOI:10.13335/j.1000-3673.pst.2022.2187
基于局部特征深度信息的绝缘子小样本缺陷检测
Few-shot Insulator Defect Detection Based on Deep Information of Local Features
摘要
Abstract
Object detection technology based on deep learning has been widely used in the insulator defect detection.However,the existing object detection algorithms are mainly based on the abundant defect samples to train the network models,which is unable to identify the defects with few samples accurately.To solve the problem of insufficient defect samples in the insulator defect detection,this paper proposes a novel few-shot insulator defect detection based on the deep information of local features.Firstly,the insulator strings are extracted using the oriented R-CNN(oriented region-based convolutional neural network).Next,the insulator string features are divided into sub-blocks and the local features are employed to realize the few-shot defect detection based on a deep EMD(earth mover's distance)network.The experimental results show that the proposed method with 2 training samples can achieve the same results as those of the existing object detection method with 200 training samples for the self-explosion defect detection of glass insulators.The mAP(mean average precision)of insulator self-explosion detection with 10 training samples is up to 0.65.The proposed few-shot defect detection method provides a new solution and an implementation method for the intelligent defect detection of the power equipment with few defect samples.关键词
绝缘子/小样本学习/目标检测/缺陷识别/卷积神经网络Key words
insulator/few-shot learning/object detection/defect identification/convolutional neural network分类
信息技术与安全科学引用本文复制引用
白晓静,谢雅祺,赵淼,吴华,张文彪,谈元鹏,叶玲玲..基于局部特征深度信息的绝缘子小样本缺陷检测[J].电网技术,2024,48(2):740-749,中插61-中插63,13.基金项目
中央高校基本科研业务费专项资金项目(2021MS016).Project Supported by the Fundamental Research Funds for the Central Universities(2021MS016). （2021MS016）
