Ya-Guang Tian, Yuan-Wei Chen, Wan Diming, Yuan Shaoguang, Mao Wandeng, Wang Chao, Chun-xiao Xu, Yifan Long
{"title":"基于CycleGAN的输电线路减振锤增强方法","authors":"Ya-Guang Tian, Yuan-Wei Chen, Wan Diming, Yuan Shaoguang, Mao Wandeng, Wang Chao, Chun-xiao Xu, Yifan Long","doi":"10.1080/19479832.2022.2033855","DOIUrl":null,"url":null,"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.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"13 1","pages":"362 - 381"},"PeriodicalIF":1.8000,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Augmentation Method for anti-vibration hammer on power transimission line based on CycleGAN\",\"authors\":\"Ya-Guang Tian, Yuan-Wei Chen, Wan Diming, Yuan Shaoguang, Mao Wandeng, Wang Chao, Chun-xiao Xu, Yifan Long\",\"doi\":\"10.1080/19479832.2022.2033855\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":46012,\"journal\":{\"name\":\"International Journal of Image and Data Fusion\",\"volume\":\"13 1\",\"pages\":\"362 - 381\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2022-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Image and Data Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19479832.2022.2033855\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2022.2033855","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Augmentation Method for anti-vibration hammer on power transimission line based on CycleGAN
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.
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.).