CASIA OpenIR
Real-time segmentation of various insulators using generative adversarial networks
Chang, Wenkai1,2; Yang, Guodong1; Yu, Junzhi1; Liang, Zize1
Source PublicationIET COMPUTER VISION
ISSN1751-9632
2018-08-01
Volume12Issue:5Pages:596-602
Corresponding AuthorChang, Wenkai(changwenkai2013@ia.ac.cn)
AbstractThe conventional inspection of fragile insulators is critical to grid operation and insulator segmentation is the basis of inspection. However, the segmentation of various insulators is still difficult because of the great differences in colour and shape, as well as the cluttered background. Traditional insulator segmentation algorithms need many artificial thresholds, thereby limiting the adaptability of algorithms. A compact end-to-end neural network, which is trained in the framework of conditional generative adversarial networks, is proposed for the real-time pixel-level segmentation of insulators. The input image is mapped to a visual saliency map, and various insulators with different poses are filtered out at the same time. The proposed two-stage training and empty samples are also used to improve the segmentation quality. Extensive experiments and comparisons are performed on many real-world images. The experimental results demonstrate superior segmentation and real-time performance. Meanwhile, the effectiveness of the proposed training strategies and the trade-off between performance and speed are analysed in detail.
Keywordimage segmentation insulators neural nets power engineering computing real-time pixel-level segmentation generative adversarial networks insulator segmentation algorithm cluttered background artificial thresholds compact end-to-end neural network visual saliency map proposed two-stage training segmentation quality
DOI10.1049/iet-cvi.2017.0591
WOS KeywordACTIVE CONTOUR MODEL ; LINE INSULATORS ; AERIAL IMAGES ; SVM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61403374] ; National Natural Science Foundation of China[61725305] ; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources[LAPS16006]
Funding OrganizationNational Natural Science Foundation of China ; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000439520600005
PublisherINST ENGINEERING TECHNOLOGY-IET
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26328
Collection中国科学院自动化研究所
Corresponding AuthorChang, Wenkai
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chang, Wenkai,Yang, Guodong,Yu, Junzhi,et al. Real-time segmentation of various insulators using generative adversarial networks[J]. IET COMPUTER VISION,2018,12(5):596-602.
APA Chang, Wenkai,Yang, Guodong,Yu, Junzhi,&Liang, Zize.(2018).Real-time segmentation of various insulators using generative adversarial networks.IET COMPUTER VISION,12(5),596-602.
MLA Chang, Wenkai,et al."Real-time segmentation of various insulators using generative adversarial networks".IET COMPUTER VISION 12.5(2018):596-602.
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