Augmentation Method for anti-vibration hammer on power transimission line based on CycleGAN

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2022-02-22 DOI:10.1080/19479832.2022.2033855

Ya-Guang Tian, Yuan-Wei Chen, Wan Diming, Yuan Shaoguang, Mao Wandeng, Wang Chao, Chun-xiao Xu, Yifan Long

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引用次数: 2

Abstract

ABSTRACT Checking the status of the power grid is very important. However, the low occurrence of defects in an actual power grid makes it difficult to collect training samples, which affects the training of defect-detection models. In this study, we proposed a method for enhancing the defective image of a power grid based on cycle-consistent adversarial networks (CycleGAN). The defective image sample dataset was expanded by fusing artificial defective samples, converted from defect-free components of samples with the trained CycleGAN model and updating its corresponding label file. Comparing the accuracy of the object detection model trained by the augmented dataset, we found a 2%–3% Average Precision (AP) improvement over baseline, and the fusing method of histogram specification reaches the best performance. In conclusion, the generative adversarial network (GAN) and its variants have considerable potential for dataset augmentation as well as scope for further improvement.

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基于CycleGAN的输电线路减振锤增强方法

摘要检查电网的状态非常重要。然而，实际电网中缺陷的发生率较低，这使得训练样本的收集变得困难，这影响了缺陷检测模型的训练。在本研究中，我们提出了一种基于循环一致对抗性网络（CycleGAN）的电网缺陷图像增强方法。通过融合人工缺陷样本，将样本的无缺陷分量与训练的CycleGAN模型转换，并更新其相应的标签文件，来扩展缺陷图像样本数据集。比较增强数据集训练的目标检测模型的精度，我们发现平均精度（AP）比基线提高了2%-3%，直方图规范的融合方法达到了最佳性能。总之，生成对抗性网络（GAN）及其变体在数据集扩充方面具有相当大的潜力，也有进一步改进的余地。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).