CASIA OpenIR
Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition
Chang, Wenkai1,2; Yang, Guodong1; Li, En1; Liang, Zize1
Source PublicationNEURAL PROCESSING LETTERS
ISSN1370-4621
2018-12-01
Volume48Issue:3Pages:1789-1800
Corresponding AuthorChang, Wenkai(changwenkai2013@ia.ac.cn)
AbstractIn this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200fps on a graphics card (GTX970) and 30fps on an embedded platform (Jetson TX1).
KeywordPower line recognition Conditional generative adversarial nets Power line inspection Hybrid robot
DOI10.1007/s11063-018-9799-3
WOS KeywordPOWER-LINE DETECTION ; MAINTENANCE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61403374]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000451460500031
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25714
Collection中国科学院自动化研究所
Corresponding AuthorChang, Wenkai
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chang, Wenkai,Yang, Guodong,Li, En,et al. Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition[J]. NEURAL PROCESSING LETTERS,2018,48(3):1789-1800.
APA Chang, Wenkai,Yang, Guodong,Li, En,&Liang, Zize.(2018).Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition.NEURAL PROCESSING LETTERS,48(3),1789-1800.
MLA Chang, Wenkai,et al."Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition".NEURAL PROCESSING LETTERS 48.3(2018):1789-1800.
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