Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Beskontaktna inteligentna tehnologija detekcije degradacije performansi željezničkoga lučnog mosta temeljena na UAV prepoznavanju slike

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read Online Read More Add to Saved list
  • Author(s): Wang, Shifu; Yang, Shaopeng; Wang, Qi; Luo, Lingfeng; Wang, Feng
  • Source:
    Građevinar; ISSN 1333-9095 (Online); ISSN 0350-2465 (Print); ISSN-L 0350-2465; CODEN GDVIAE; Volume 77; Issue 01.
  • Document Type:
    Electronic Resource
  • Online Access:
    https://doi.org/10.14256/JCE.3925.2023
    https://hrcak.srce.hr/329021
    https://hrcak.srce.hr/file/475730
    https://hrcak.srce.hr/file/475731
    info:eu-repo/semantics/altIdentifier/doi/10.14256/JCE.3925.2023
  • Additional Information
    • Additional Titles:
      Non-contact intelligent detection technology for railway arch bridge performance degradation based on UAV Image recognition
    • Publisher Information:
      Croatian association of civil engineers 2025
    • Abstract:
      Mostovi su ključne komponente projekata brzih željeznica, a njihov konstrukcijski integritet znatno utječe na operativnu sigurnost brzih željeznica. U ovome radu predstavljena je tehnologija beskontaktne inteligentne detekcije za procjenu propadanja željezničkih mostova za velike brzine pomoću prepoznavanja slike bespilotnih letjelica (UAV). Metodologija uključuje prikupljanje slikovnih podataka pomoću UAV-a i digitalne kamere te njihovu tehničku obradu za generiranje dosljednih podataka oblaka točaka. Naknadno se ti podaci integriraju u jedinstveni model oblaka točaka poravnanjem oblaka točaka. Konačno, rafinirani trodimenzionalni (3D) model željezničkog mosta za velike brzine razvijen je spajanjem heterogenih podataka kroz 3D rekonstrukciju uživo. Metoda ima prednosti poput velike brzine otkrivanja i manje zahtjeva za osobljem. Ta se tehnologija može primjenjivati za dnevno praćenje tehničke osnove i za obavljanje dnevne inspekcije uz manji broj radnika. Empirijski rezultati pokazuju da ta metoda inspekcije nije ograničena točkama svjetlarnika i da pruža vrlo učinkovit odraz stanja mosta u stvarnome vremenu. Točnost prepoznavanja i raspon snimanja slike zadovoljavaju zahtjeve inspekcije za rad i održavanje željezničkih mostova za velike brzine.
      Bridges are crucial components of high-speed railway projects, and their structural integrity significantly impacts the operational safety of high-speed railways. This paper introduces a non-contact intelligent detection technology for assessing the deterioration of high-speed railway bridges using unmanned aerial vehicle (UAV) image recognition. The methodology involves collecting image data using a UAV and digital camera and processing them technically to generate consistent point-cloud data. Subsequently, these data are integrated into a unified point-cloud model through point-cloud alignment. Finally, a refined three-dimensional (3D) model of a high-speed railway bridge was developed by fusing heterogeneous data through live 3D reconstruction. The method has the advantages of high detection speed and fewer personnel requirements; this technology can be used for daily monitoring of the technical basis and can arrange a small number of personnel to complete the daily inspection. The empirical results demonstrate that this inspection method is not constrained by skylight points and provides a real-time and highly efficient reflection of the conditions of the bridge. The recognition accuracy and image acquisition range satisfy the inspection requirements for the operation and maintenance of high-speed railway bridges.
    • Subject Terms:
      željeznički most za velike brzine; oštećenja na mostu; beskontaktno mjerenje; bespilotna letjelica; high-speed railway bridge; bridge faults; non-contact measurement; UAV; text; info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion
    • Availability:
      Open access content. Open access content
      info:eu-repo/semantics/openAccess
      Full-text papers can be used for personal or educational purposes only. Authors' and publisher's copyrights must be acknowledged.
    • Note:
      application/pdf
      Croatian
      English
    • Other Numbers:
      HRCAK oai:hrcak.srce.hr:329021
      1520285971
    • Contributing Source:
      HRCAK PORTAL ZNANSTVENIH CASOPISA REPUB
      From OAIster®, provided by the OCLC Cooperative.
    • Accession Number:
      edsoai.on1520285971
HoldingsOnline