A conceptual study of infrared and visible-light image fusion methods for three-dimensional object reconstruction

Marcellino, G.; Borges, Vicente K.; Pinto, Tiago

doi:10.1117/12.2527428

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…the position and orientation of the camera relative to the scene for each acquisition, must be carefully planned. For better points correspondence between images, about 80% image overlap and sequence acquisitions are recommended (Luhman et al, 2011)(Agisoft, 2020) (Atkinson, 1996) (Marcellino et al, 2019).…”

Section: Image Acquisition Proceduresmentioning

confidence: 99%

Targetless Photogrammetry Network Simulation for Inspection Planning in Oil and Gas Industry

Buschinelli

Salazar

Regner

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The oil and gas offshore industry demands regular inspections of components and structures that are subjected to extreme operational and environmental conditions. In this context, risers are pipelines that transport mainly oil, gas, water, and cables between submarine structures and the surface offshore platform, in the portion not touching the ocean floor. The emerged part of these risers is typically inspected by industrial climbing, which is a very time-consuming activity, has high operational costs, is dangerous and has a strong dependence on inspector skills. Remotely Piloted Aircraft Systems (RPAS) have been recently used for visual inspection of risers, however, no quantitative or geometrical evaluation has been conducted using this kind of image acquisition yet. An image-based measurement technique, such as close-range photogrammetry, can provide a 3D reconstruction using images, but a series of requisites is mandatory to achieve good results as image acquisition sequence, overlap, camera positioning network, spatial resolution and object texture in non-prepared and targetless scenes. The analysis of different image acquisition strategies using a real RPAS is too difficult because it demands a lot of time, good weather, daylight, and a scene similar to where risers are installed. An alternative is to use simulation. In this paper a ROS/Gazebo simulation is described and used to create a realistic textured 3D virtual environment of the platform, risers and RPA, providing a fast and low-cost solution to simulate different RPA trajectories for photogrammetry image acquisition in targetless scenes. These trajectories are evaluated by comparing the measured risers through photogrammetry to its CAD/simulated model. Since the scene is not prepared, the RPA position/orientation or a stereo vision setup can be used to set scale to the measurement result. The best trajectory found during simulations was also evaluated in a real experiment.

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Section: Image Acquisition Proceduresmentioning

confidence: 99%

Targetless Photogrammetry Network Simulation for Inspection Planning in Oil and Gas Industry

Buschinelli

Salazar

Regner

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…One of the biggest drawbacks of the real dataset used to perform the research, is that the images were acquired only for visual inspection without considering photogrammetry requisites, as network design, image overlapping, spatial resolution, type of sensor and object texture (Thomas Luhman, Stuart Robson, Stephen Kyle, 2011) (Atkinson, 1996) (Marcellino et al, 2019),. And as mentioned earlier, this influences the result of 3D reconstructions.…”

Section: Virtual Datasetmentioning

confidence: 99%

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

Salazar

Buschinelli

Marcellino

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The purpose of this paper is to show how deep learning techniques, based on CNNs, can contribute to photogrammetry process to perform geometric inspections of risers on offshore platforms. The photogrammetry process has a problematic related to the relative movements presented in the scene where the images are being acquired (dynamic photogrammetry). As an alternative solution, this work proposes the use of the YOLOv2 architecture, because this detector complies with some requirements of speed and good performance considering the functional requisites of the study executed. Thus, the purpose of this model is to detect risers and i-tubes on offshore platforms, then extract an inspection riser from the scene. Finally, with the images obtained, a 3D reconstruction is performed, followed by the results’ analyses.

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“…The image fusion procedure adopted in this work is based on the one presented in [19], but with modification regarding several parameters and also the factorial analysis, which are described further.…”

Section: Image Fusion Proceduresmentioning

confidence: 99%

“…The fusion of infrared and VL devices is also a frequent topic in this sense [6,[14][15][16][17]. However, the particular case of data fusion called image fusion is not typically considered, besides the alternative explored by the authors in [19]. This work described an approach to fuse LWIR and VL images before the three-dimensional reconstruction procedure.…”

Section: Introductionmentioning

confidence: 99%

Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

Marcellino

Rosa

Pinto

2020

Meas. Sci. Technol.

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This work proposes evaluating statistically the metrological performance of three-dimensional reconstructions built with fused long-wavelength infrared (LWIR) and visible-light (VL) images. The image fusion procedure was essentially based on two-dimensional wavelet transform and two pixel-level fusion rules: the maximum intensity level, presented in a previous work of the authors, and a new fusion rule, which replaces the VL information with the LWIR information in the region of the measured object on the images. The reconstructions of a translucent cube were performed with a point triangulation-based procedure and its dimension measurements were employed as evaluation criteria. The results show that the fused images have more contrast but also more artifacts. The fusion procedures generated denser reconstructions with at least 34.83% more points. Considering the metrological result, reconstructions with only visible-light images resulted in maximal 89.31% less measurement bias but at least 47.25% more uncertainty than the fusion ones. The new fusion rule provided the best results, with more points in the dense cloud and lower uncertainty. The work is important to provide a metrologically viable alternative for three-dimensional reconstruction of objects in situations of low contrast or poor texture information in the visible spectrum, and in which no target can be applied to the inspected part.

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A conceptual study of infrared and visible-light image fusion methods for three-dimensional object reconstruction

Cited by 3 publications

References 5 publications

Targetless Photogrammetry Network Simulation for Inspection Planning in Oil and Gas Industry

Targetless Photogrammetry Network Simulation for Inspection Planning in Oil and Gas Industry

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

Metrological analysis of the three-dimensional reconstruction based on close-range photogrammetry and the fusion of long-wave infrared and visible-light images

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