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dc.contributor.authorNguyen, Van Nhan
dc.contributor.authorJenssen, Robert
dc.contributor.authorRoverso, Davide
dc.date.accessioned2021-12-03T12:25:06Z
dc.date.available2021-12-03T12:25:06Z
dc.date.issued2021-10-29
dc.description.abstractIn unmanned aerial vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in post-processing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional, and it consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the physically based rendering approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real time. This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection.en_US
dc.identifier.citationNguyen, Jenssen, Roverso. LS-Net: fast single-shot line-segment detector. Machine Vision and Applications. 2021;32(1)en_US
dc.identifier.cristinIDFRIDAID 1917995
dc.identifier.doi10.1007/s00138-020-01138-6
dc.identifier.issn0932-8092
dc.identifier.issn1432-1769
dc.identifier.urihttps://hdl.handle.net/10037/23258
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalMachine Vision and Applications
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400en_US
dc.titleLS-Net: fast single-shot line-segment detectoren_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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