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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TP312

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广东电网有限责任公司科技攻关项目(031200KK52160013)


Defect Detection Method of Transmission Lines and Towers Based on GAN and Improved RPN
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    摘要:

    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 Network,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。

    Abstract:

    In order to reduce the interference of noise signal and device performance in the process of transmission line tower defect detection, and improve the accuracy and efficiency of transmission line tower defect detection. A defect detection method of transmission line towers based on Generative Adversarial Network (GAN) and Improved Region Proposal Network (RPN) is proposed. GAN is used to collect the salient images of transmission line towers, and the semi-soft threshold function model is used to eliminate the noise in the images, so as to avoid the influence of noise on the defect detection process. The contour features of transmission line tower image are extracted by random forest decision tree, and RPN is improved based on multi-scale algorithm. The features are input into the improved RPN model, and the defect detection of transmission line tower is completed by defect location and segmentation. The test results show that the proposed method has high accuracy and good defect detection effect and efficiency, which is beneficial to improve the quality of transmission line tower defect detection and reduce the occurrence of power accidents.

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练文卓,黄伟杰,黄滔,等. 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[J]. 科学技术与工程, 2024, 24(13): 5436-5442.
Lian Wenzhuo, Huang Weijie, Huang Tao, et al. Defect Detection Method of Transmission Lines and Towers Based on GAN and Improved RPN[J]. Science Technology and Engineering,2024,24(13):5436-5442.

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  • 收稿日期:2023-06-24
  • 最后修改日期:2024-03-04
  • 录用日期:2023-09-27
  • 在线发布日期: 2024-05-17
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