A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery
The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera-mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impractical without extensive metadata or computer-vision-based algorithms.
In this paper, we propose an end-to-end method for automated structural inspection damage analysis. Using automated object detection and segmentation we accurately localize defects, bridge utilities and elements. Next, given the high overlap in UAV imagery, points of interest are extracted, and defects are located and matched throughout the image database, considerably reducing data redundancy while maintaining a detailed record of the defects.
Our technique not only enables fast and robust damage analysis of UAV imagery, as we show herein, but is also effective for analyzing manually acquired images.
Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery
The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera-mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impractical without extensive metadata or computer-vision-based algorithms.
In this paper, we propose an end-to-end method for automated structural inspection damage analysis. Using automated object detection and segmentation we accurately localize defects, bridge utilities and elements. Next, given the high overlap in UAV imagery, points of interest are extracted, and defects are located and matched throughout the image database, considerably reducing data redundancy while maintaining a detailed record of the defects.
Our technique not only enables fast and robust damage analysis of UAV imagery, as we show herein, but is also effective for analyzing manually acquired images.
Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery
Lecture Notes in Civil Engineering
Pellegrino, Carlo (editor) / Faleschini, Flora (editor) / Zanini, Mariano Angelo (editor) / Matos, José C. (editor) / Casas, Joan R. (editor) / Strauss, Alfred (editor) / Oring, Alon (author)
International Conference of the European Association on Quality Control of Bridges and Structures ; 2021 ; Padua, Italy
Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures ; Chapter: 142 ; 1251-1260
2021-12-12
10 pages
Article/Chapter (Book)
Electronic Resource
English
Predictive maintenance , Preventive maintenance , Deep learning , Object detection , Image matching , Damage assessment , Defect matching Engineering , Building Construction and Design , Engineering Economics, Organization, Logistics, Marketing , Risk Management , Fire Science, Hazard Control, Building Safety , Building Materials
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