首页|基于生成式对抗网络和改进区域建议网络的输电线路杆塔缺陷检测方法

基于生成式对抗网络和改进区域建议网络的输电线路杆塔缺陷检测方法

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为了减少输电线路杆塔缺陷检测过程中受噪声信号和装置性能等因素的干扰,提高输电线路杆塔缺陷检测的正确率和检测效率.提出一种基于生成式对抗网络(generative adversarial networks,GAN)和改进区域建议网络(region proposal net-work,RPN)的输电线路杆塔缺陷检测方法.采用GAN采集输电线路杆塔的显著性图像,并利用半软阈值函数模型剔除图像中的噪声,避免噪声对缺陷检测过程产生影响.通过随机森林决策树提取输电线路杆塔图像的轮廓特征,基于多尺度算法对RPN进行改进,将特征输入到改进RPN模型中,通过缺陷的定位、分割完成输电线路杆塔的缺陷检测.试验结果表明,所提方法的输电线路杆塔缺陷检测正确率较高,具有较好的缺陷检测效果和检测效率,从而有利于提高输电线路杆塔缺陷检测的质量,减少电力事故的出现.
Defect Detection Method for Transmission Line Towers Based on GAN and Improved RPN
To enhance the accuracy and efficiency of defect detection in transmission line towers by reducing interference from noise signals and device performance,a defect detection method utilizing generative adversarial networks(GAN)and an improved region proposal network(RPN)was proposed.GAN was employed to capture significant images of transmission line towers,while a semi-soft thresholding function model was utilized to remove noise from the images and mitigate its impact on the defect detection process.The contour features of transmission line tower images were extracted using a random forest decision tree,and an enhanced RPN based on a multiscale algorithm was introduced.By inputting these features into the improved RPN model,defect localization and segmentation were performed for accurate defect detection in transmission line towers.Experimental results demonstrate the high accuracy,effectiveness,and efficiency of the proposed approach,thereby contributing to better quality control and a reduced occurrence of power accidents in transmission line towers.

generative adversarial networkimproved region proposal networktransmission linesignificant imagessemi-soft threshold function modelrandom forest decision tree

练文卓、黄伟杰、黄滔、谢榕昌、周俊宏、江润洲

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广东电网有限责任公司惠州供电局,惠州 516000

广东电网有限责任公司,广州 510000

生成式对抗网络 改进区域建议网络 输电线路 显著性图像 半软阈值函数模型 随机森林决策树

广东电网有限责任公司科技攻关计划

031200KK52160013

2024

10.12404/j.issn.1671-1815.2304652

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(13)
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