Dataset Information

Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry.

ABSTRACT: Oscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by terrestrial photogrammetry using non-metric cameras. Tedious camera calibration procedures are eliminated by using a grid target that allows geometric correction to be performed to the frame's region of interest at which oscillations are monitored. Region-based convolutional neural networks (Faster R-CNN) techniques are adopted to minimize the light exposure limitations, commonly constraining applications of terrestrial photogrammetry. The proposed monitoring procedure is tested at outdoor conditions to check its reliability and accuracy and examining the effect of using Faster R-CNN on monitoring results. The proposed artificial intelligence (AI) aided oscillation monitoring allowed sub-millimeter accuracy monitoring with observation rates up to 60 frames per second and gained the benefit of high optical zoom offered by market available bridge cameras to monitor oscillation of targets 100 m apart with high accuracy.

SUBMITTER: El-Kadi O

PROVIDER: S-EPMC7218887 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry.

El-Kadi Omar O El-Shazly Adel A Nassar Khaled K

Sensors (Basel, Switzerland) 20200415 8

Oscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by terrestrial photogrammetry using non-metric cameras. Tedious camera calibration procedures are eliminated by using a grid target that allows geometric correction to be performed to the frame's region ...[more]

PMID: 32326488

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Project description:Annual trunk increments are essential for short-term analyses of the response of trees to various factors. For instance, based on annual trunk increments, it is possible to develop and calibrate forest growth models. We investigated the possibility of estimating annual trunk increments from the terrestrial structure from motion (SfM) photogrammetry. Obtaining the annual trunk increments of mature trees is challenging due to the relatively small growth of trunks within one year. In our experiment, annual trunk increments were obtained by two conventional methods: measuring tape (perimeter increment) at heights of 0.8, 1.3, and 1.8 m on the trunk and increment borer (diameter increment) at a height of 1.3 m on the trunk. The following tree species were investigated: Fagus sylvatica L. (beech), Quercus petraea (Matt.) Liebl. (oak), Picea abies (L.) H. Karst (spruce), and Abies alba Mill (fir). The annual trunk increments ranged from 0.9 cm to 2.4 cm (tape/perimeter) and from 0.7 mm to 3.1 mm (borer/diameter). The data were collected before- and after-vegetation season, besides the data collection increment borer. When the estimated perimeters from the terrestrial SfM photogrammetry were compared to those obtained using the measuring tape, the root mean square error (RMSE) was 0.25-1.33 cm. The relative RMSE did not exceed 1% for all tree species. No statistically significant differences were found between the annual trunk increments obtained using the measuring tape and terrestrial SfM photogrammetry for beech, spruce, and fir. Only in the case of oak, the difference was statistically significant. Furthermore, the correlation coefficient between the annual trunk increments collected using the increment borer and those derived from terrestrial SfM photogrammetry was positive and equal to 0.6501. Terrestrial SfM photogrammetry is a hardware low-demanding technique that provides accurate three-dimensional data that can, based on our results, even detect small temporal tree trunk changes.

Dataset Information

Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry.

Publications

Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry.

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