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A survey of emerging technologies for the future of routine visual inspection of bridge structures
Published in Joan-Ramon Casas, Dan M. Frangopol, Jose Turmo, Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, 2022
D.T. Nepomuceno, P.J. Vardanega, T. Tryfonas, M. Pregnolato, J. Bennetts, G. Webb
A 360° camera can simultaneously capture an image in all directions to give a literal 360° view around a certain point. Such cameras are often comprised of multiple wide-angle lenses, with the image from each lens being automatically stitched together to create one spherical image (Huang et al. 2017). The resulting image can be used in a virtual reality (VR) setting by inspectors, enabling convenient inspection of recorded areas by ‘looking around’ the spherical image (Tan et al. 2018). This provides an alternative to the traditional ‘point-and-shoot’ method of standard cameras, which can simplify optimal inspection paths around a structure. For example, a high-resolution 360° camera unit mounted on a moving vehicle would have the potential to efficiently gather high-quality image data from the underside of a bridge. 360° cameras are readily accessible, but their use for bridge management is relatively unexplored, with only a few studies reported (e.g. Nishimura et al. 2012, Hada et al. 2017, Humpe 2020).
Examples of future applications
Published in Michael O’Byrne, Bidisha Ghosh, Franck Schoefs, Vikram Pakrashi, Image-Based Damage Assessment for Underwater Inspections, 2019
Bidisha Ghosh, Michael O’Byrne, Franck Schoefs, Vikram Pakrashi
While multiple camera rigs are more expensive, the image quality is superior. Additionally, many of these rigs are aimed at the sports/outdoors enthusiast market and, consequently, they are rugged and waterproof. An example of a multi-camera rig is shown in Figure 9.10(a), which features 6 action cameras housed in a casing. It is a fully integrated solution with all cameras automatically synchronized. An example of a spherical image is shown in Figure 9.10(b). A drawback of creating spherical images/video with multiple cameras is the risk of artifacts appearing when the constituent images are stitched together to form one complete spherical image. Effective camera calibration is crucial here for mitigating this problem. Moreover, the stitching process can be a bottleneck, especially if dealing with very high-resolution imagery.
A triangle mesh-based corner detection algorithm for catadioptric images
Published in The Imaging Science Journal, 2018
A spherical image is defined by back-projecting a catadioptric image onto the unit sphere, which is used as a projection plane of the projection model described above. According to the projection model of catadioptric cameras, all incident rays, which form a spherical image, pass through a single effective viewpoint. Since the resolution of an image is defined as the ratio of an infinitesimal area on the image plane to an infinitesimal solid angle of the world [1, 7], the resolution of a spherical image is spatially uniform. Therefore, generation of spherical images can address problems of catadioptric image processing related to non-uniform resolution of images.