基于深度学习的在役高压电缆缺陷计算机断层扫描检测  

In-Service High-Voltage Cable Defect Detection Using Computed Tomography Based on Deep Learning

作　　者：何朝良 晏婷 马天宇 段晓礁[1,2] He Chaoliang;Yan Ting;Ma Tianyu;Duan Xiaojiao(Key Laboratory of Optoelectronic Technology&System,Ministry of Education,Chongqing University,Chongqing 400044,China;Industrial CT Non-Destructive Testing Engineering Research Center,Ministry of Education,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学光电技术及系统教育部重点实验室,重庆400044 [2]重庆大学工业CT无损检测教育部工程研究中心,重庆400044

出　　处：《光学学报》2025年第2期220-230,共11页Acta Optica Sinica

基　　金：国家重点研发计划(2022YFF0706400);中央高校基本科研业务费(2024CDJYXTD-009)。

摘　　要：城市化进展的加快对电力输送提出更高的要求,在役电缆的无损检测成为一个研究热点,然而传统方法在检测电缆缓冲层中微小缺陷结构方面的效果十分有限。采用射线源平移局部计算机断层扫描(L-STCT)成像方法来获取电缆图像数据,设计了一种基于Cascade区域卷积神经网络(R-CNN)的改进网络。实验结果表明所设计的网络在所制作的L-STCT数据集下的精确率和召回率都有显著提升,进一步以RCT数据集为基础进行知识迁移,最终网络精确率达90.1%,召回率达95.9%。该研究为电缆内部缺陷无损检测提供了一种有效的方案。Objective High-voltage cables are crucial for constructing a safe and reliable urban power grid amid rapid urbanization.Damage to these cables can severely influence power transmission,potentially causing safety issues and economic losses.Maintaining high-voltage cables is challenging and costly,highlighting the need for efficient,non-destructive defect detection methods.Traditional methods,such as partial discharge detection,high-order harmonic analysis,and broadband impedance testing,struggle to accurately detect buffer layer ablation defects and locate specific defect positions.In contrast,computed tomography(CT)imaging provides a more intuitive visualization of defects and can quantify buffer layer ablation sizes from certain angles.However,conventional circular CT(RCT)techniques are unsuitable for detecting in-service high-voltage cables in confined spaces.In this study,we address the challenges of in-service cable detection by utilizing L-STCT technology combined with a deep learning-based method,using an improved Cascade R-CNN(regionconvolutional neural network)to enhance the recall rate.The proposed method offers an effective solution for the nondestructive detection of internal cable defects.Methods We utilize L-STCT scanning to detect cable defects,with the SIRT algorithm used for image reconstruction.The resulting images are preprocessed to create an L-STCT dataset.To extract deeper features from the images,the ResNeXt101 with 64 filters is integrated into the Cascade R-CNN as the backbone for feature extraction,mitigating issues such as gradient vanishing and overfitting caused by excessive network depth.An attention mechanism is incorporated to help the network focus on defect-related information,improving its resistance to noise and artifacts.In addition,the EFPN module is introduced to enhance the detection of small targets while preserving other valuable information,enabling multiscale feature extraction.The original position regression function is replaced with the Focal-EIoU loss function for mo

关 键 词：工业计算机断层扫描 高压电缆 深度学习 缺陷检测 迁移学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]