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 more accurate localization,forming an optimized Cascade R-CNN.Although RCT cannot be directly applied to in-service cable detection,the similarity between RCT and L-STCT datasets allows for transfer learning;the network is pre-trained on the RCT dataset and then fine-tuned on the L-STCT dataset to further improve the recall rate of the network.Results and Discussions Ablation experiments confirm that the improved Cascade R-CNN network exhibits enhanced noise and artifact resistance with the introduction of the attention mechanism,while the EFPN module effectively identifies small defect structures.Compared to the original network,the optimized version shows significant improvements in accuracy and recall,demonstrating the algorithm’s suitability for cable defect detection(Table 3).The performance of the enhanced algorithm surpasses that of many mainstream target detection networks under the same dataset conditions(Table 4).The approach also offers advantages such as lower dataset and hardware requirements,making it highly practical.Transfer learning results indicate that pre-training the network on the RCT dataset improves its performance on the L-STCT dataset.Following transfer learning,the network achieves higher accuracy and recall rate comparable to those obtained with the original network(Table 5),confirming the effectiveness and applicability of the improved network.Conclusions In this study,we propose an enhanced cable defect detection algorithm based on the Cascade R-CNN,tailored to address challenges such as background noise and the detection of small targets.The algorithm performs well on the L-STCT dataset,achieving an accuracy of 0.884 and a recall rate of 0.927.With the RCT dataset pre-training,accuracy improves to 0.901,and recall reaches 0.959.The results demonstrate that while RCT cannot be directly applied for in-service cable defect detection,the similarities between the RCT and L-STCT datasets facilitate transfer learning,guiding the network to more effectively detect defects.The proposed algorithm offers a high defect recognition accuracy and a low miss rate,making it valuable for detecting defects in in-service cable buffer layers.
作者
何朝良
晏婷
马天宇
段晓礁
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)
出处
《光学学报》
北大核心
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)。
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
industrial computed tomography
high-voltage cable
deep learning
defect detection
transfer learning
