4.5 Article

Feature-based image stitching for panorama construction and visual inspection of structures

期刊

SMART STRUCTURES AND SYSTEMS
卷 28, 期 5, 页码 661-673

出版社

TECHNO-PRESS
DOI: 10.12989/sss.2021.28.5.661

关键词

computer vision; image stitching; multi-level constraint criterion; structural health monitoring; visual inspection

资金

  1. National Natural Science Foundation of China [51878483]
  2. Key Laboratory of Shock and Vibration of Engineering Materials and Structures, Sichuan Province [19kfgk03]
  3. Shanghai Qi Zhi Institute [SYXF0120020109]
  4. Peak Discipline Construction Project of Shanghai [2021-CE-03]

向作者/读者索取更多资源

This study presents a feature-based image stitching method for panorama construction and visual inspection of building structures. The methodology framework is inspection-oriented with optimized inlier distribution. The reliability of the proposed feature-based stitching approach is parametrically studied with different setups of input images.
This study presents a feature-based image stitching method with multi-level constraint criterion for panorama construction and visual inspection of building structures. The comparison of global view and local resolution over building exterior is discussed regarding practical implementation. An inspection-oriented methodology framework with optimized inlier distribution is designed for generating a feasible and reliable building panorama by using ordinary optic images. Two illustrative examples, including an earthquake-damaged masonry wall and a high-rise building with stone curtain walls, are experimentally investigated. The severely developed structural crack is fully mapped with stitched image and extracted in preparation for further quality evaluation. The curtain wall of the high-rise building is successfully constructed by using UAV-based images. The panorama quality is further compared with commercial stitching software and several improvements are illustrated in the particular case. In addition, the reliability of the proposed feature-based stitching approach is parametrically studied with different setups of input images.

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