Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model

Simple item page
dc.contributor.authorWang, Xin
dc.contributor.authorJiang, Guimin
dc.contributor.authorHu, Jue
dc.contributor.authorSfarra, Stefano
dc.contributor.authorMostacci, Miranda
dc.contributor.authorKouis, Dimitrios
dc.contributor.authorYang, Dazhi
dc.contributor.authorFernandes, Henrique
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorMaldague, Xavier
dc.contributor.authorGai, Yonggang
dc.contributor.authorZhang, Hai
dc.date.accessioned2024-10-25T10:30:49Z
dc.date.available2024-10-25T10:30:49Z
dc.date.freetoread2024-10-25
dc.date.issued2024-09-16
dc.date.pubOnline2024-09-16
dc.description.abstractIn recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.
dc.description.journalNameHeritage Science
dc.description.sponsorshipMinistry of Education, Universities and Research, Ministry of Science and Technology of the People's Republic of China
dc.description.sponsorshipThis work is supported by the Ministry of Science and Technology of China (MOST) through the National Key R&D Program (Grant No. 2023YFE0197800). This work is also supported by the Ministry of University and Research of Italy (Grant No. PGR02016).
dc.identifier.citationWang X, Jiang G, Hu J, et al., (2024) Automatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model. Heritage Science, Volume 12, September 2024, Article number 329
dc.identifier.eissn2050-7445
dc.identifier.elementsID553971
dc.identifier.issn2050-7445
dc.identifier.paperNo329
dc.identifier.urihttps://doi.org/10.1186/s40494-024-01441-9
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23117
dc.identifier.volumeNo12
dc.languageEnglish
dc.language.isoen
dc.publisherSpringer
dc.publisher.urihttps://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-024-01441-9
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEfficient channel attention
dc.subjectFaster R-CNN
dc.subjectDefect detection
dc.subjectDeep learning
dc.subjectInfrared thermography
dc.subject4302 Heritage, Archive and Museum Studies
dc.subject43 History, Heritage and Archaeology
dc.subject4301 Archaeology
dc.subject4302 Heritage, archive and museum studies
dc.titleAutomatic defect detection in infrared thermal images of ancient polyptychs based on numerical simulation and a new efficient channel attention mechanism aided Faster R-CNN model
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-03

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Automatic_defect_detection_in_infrared_thermal_images-2024.pdf
Size:
3.16 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)