Tag recognition: A new methodology for the structural monitoring of cultural heritage. (October 2018)

• Record Type: Journal Article
• Authors: Mangini, Fabio; D'Alvia, Livio; Del Muto, Mauro; Dinia, Lorenzo; Federici, Enrico; Palermo, Eduardo; Del Prete, Zaccaria; Frezza, Fabrizio
• Is Part Of: Measurement. Volume 127(2018)
• Page Start: 308
• Page End: 313
• Publication Date: 2018-10
• Subjects: Tag recognition -- Gaussian fitting -- Crack measurement -- Monitoring cultural heritage; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.06.003
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An innovative crack measurement methodology called Tag Recognition has been proposed. The methodology is characterized by a minimum invasiveness preserving the aesthetic appearance. The core of the system is composed of two small adhesive tags and a camera acquiring images of the tags. The relative distance between the optical tags is determined using advanced least-square algorithms. The proposed methodology showed a maximum relative error of 0.1 pixel. Abstract: In this paper, a new methodology for measuring the cracking in the field of Structural Health Monitoring (SHM) of cultural heritage, is presented. The minimum invasiveness of this methodology permits to preserve the aesthetic appearance, a fundamental requirement in monitoring of cultural heritage. The core of the acquisition system is composed by two small adhesive tags to be attached on the artwork surface, and a high-resolution camera acquiring images of the tags. The relative distance between the optical tags for determining is determined using advanced least-squares fitting of quadratic curves and surfaces algorithms for the objective function. Here, the bi-dimensional Gaussian as objective function has been taken into account, in order to find the best configuration for determining the fitting parameters, useful for the SHM. We ran a simulation for tuning fitting algorithm parameters. Then we validated the methodology through an experimental session.