3D reconstruction of rail tracks based on fusion of RGB and infrared sensors

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dc.contributor.authorWang, Yizhong
dc.contributor.authorKuang, Boyu
dc.contributor.authorDurazo, Isidro
dc.contributor.authorZhao, Yifan
dc.date.accessioned2024-11-14T12:53:08Z
dc.date.available2024-11-14T12:53:08Z
dc.date.freetoread2024-11-14
dc.date.issued2024-08-28
dc.date.pubOnline2024-10-23
dc.description.abstractRail tracks, an essential part of the rail system, have remarkably demanded thorough inspections amid rising passenger volumes and high-speed rail development. Non-destructive testing (NDT), without disrupting train operations, aims to mitigate risks by employing safe physical properties like sound, electromagnetic, and light. However, each NDT technique is sensitive to specific damage types, offering limited diagnostic perspectives and placing considerable requirements on operators, resulting in a high cognitive load. To improve the above situation, this study proposes an innovative approach for rail inspection by developing a 3D RGB-T model that combines Visual Testing (VT) and Thermal Inspection (TI) through image registration, 3D reconstruction, sensor fusion, and non-destructive testing (NDT). Their fusion facilities a complementary assessment of rail tracks by capturing both surface texture and thermal radiation to identify damages effectively. The introduction of a novel RGB and IR registration method enables the spatial alignment of images from both, reconstructing the 3D RGB-T model. This model broadens the detection scope beyond the limitations of singular NDT methods, utilizing complementary data to locate and assess the damage extent effectively and accurately. This integrated approach reduces training requirements, minimizes human errors, and provides a clear and interpretable visualization of track conditions.
dc.description.conferencename2024 29th International Conference on Automation and Computing (ICAC)
dc.format.extent374-379
dc.identifier.citationWang Y, Kuang B, Durazo I, Zhao Y. (2024) 3D reconstruction of rail tracks based on fusion of RGB and infrared sensors. In: 2024 29th International Conference on Automation and Computing (ICAC), 28 - 30 Aug 2024, Sunderland, United Kingdom, pp. 374-379
dc.identifier.elementsID555497
dc.identifier.isbn979-8-3503-6089-9
dc.identifier.urihttps://doi.org/10.1109/icac61394.2024.10718778
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23189
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/abstract/document/10718778
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.subject33 Built Environment and Design
dc.subjectmulti-sensor information fusion
dc.subjectrail inspection
dc.subject3D reconstruction
dc.subjectnon-destructive testing
dc.subjectpulsed thermography
dc.title3D reconstruction of rail tracks based on fusion of RGB and infrared sensors
dc.typeConference paper
dcterms.coverageSunderland, United Kingdom
dcterms.temporal.endDate30-Aug-2024
dcterms.temporal.startDate28-Aug-2024

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