摘要
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 inspection 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).
•A new autonomous dynamic line-scan non-destructive inspection system combining with continuous-wave terahertz (CW THz) imaging and unsupervised exposure fusion was developed.•Line-scanner CW THz technology for inspection was poorly documented in the open literature. The newly developed system is aimed at producing clear external and internal maps for industrial non-destructive inspection.•A new unsupervised fusion algorithm UDFF was proposed for multi-focus image fusion to correct the inaccurate image acquisition caused by unbalanced line-scan exposure source.•The comparisons exhibit the superiority of UDFF to the state-of-the-art algorithms, especially for background noise suppression and defect details preserving.•Experiments and analyses certify the capability of the speedy detection (up to 49.2 mm/s) for the newly developed system.