The investigation of cultural heritage objects is commonly carried out by using non-destructive inspection techniques. In fact, due to the artistic peculiarities of cultural heritage objects, conventional contact-type in-spection techniques such as ultrasonic scan cannot always be applied. The objective of this study is to develop a new autonomous dynamic line-scan continuous-wave terahertz (CW THz) non-destructive inspection system combined with long-wave infrared (LWIR) thermography. The newly developed system is aimed at producing clear external and internal maps for wooden objects inherent to the cultural patrimony. Additionally, a new unsupervised fusion algorithm is also proposed for multi-energy density data fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. The algorithm is designed in an encoder -decoder deep learning structure with dense blocks. Finally, it is worth mentioning that the newly developed system has the capability of speedy detection (up to 49.2 mm/s) and fast in-line processing (of the order of seconds).

Autonomous dynamic line-scan continuous-wave terahertz non-destructive inspection system combined with unsupervised exposure fusion

Sfarra, S;
2022-01-01

Abstract

The investigation of cultural heritage objects is commonly carried out by using non-destructive inspection techniques. In fact, due to the artistic peculiarities of cultural heritage objects, conventional contact-type in-spection techniques such as ultrasonic scan cannot always be applied. The objective of this study is to develop a new autonomous dynamic line-scan continuous-wave terahertz (CW THz) non-destructive inspection system combined with long-wave infrared (LWIR) thermography. The newly developed system is aimed at producing clear external and internal maps for wooden objects inherent to the cultural patrimony. Additionally, a new unsupervised fusion algorithm is also proposed for multi-energy density data fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source. The algorithm is designed in an encoder -decoder deep learning structure with dense blocks. Finally, it is worth mentioning that the newly developed system has the capability of speedy detection (up to 49.2 mm/s) and fast in-line processing (of the order of seconds).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/204704
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