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

IF 2.6 1区 艺术学 Q2 CHEMISTRY, ANALYTICAL Heritage Science Pub Date : 2024-09-16 DOI:10.1186/s40494-024-01441-9
Xin Wang, Guimin Jiang, Jue Hu, Stefano Sfarra, Miranda Mostacci, Dimitrios Kouis, Dazhi Yang, Henrique Fernandes, Nicolas P. Avdelidis, Xavier Maldague, Yonggang Gai, Hai Zhang
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Abstract

In 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.

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基于数值模拟和新型高效通道关注机制辅助的快速 R-CNN 模型,自动检测古代多联画红外热图像中的缺陷
近年来,古代文化遗产的保存和保护需要先进的非破坏性检测方法,以尽量减少对艺术品的潜在损害。因此,本研究旨在开发一种利用红外热成像技术检测古代壁画缺陷的先进方法。测试对象是两幅多联画样本,复制了 Pietro Lorenzetti(1280/85-1348 年)创作于 14 世纪的艺术作品,其中包含不同的颜料和人工诱导的缺陷。为了应对这些挑战,我们提出了一种自动缺陷检测模型,将数值模拟和图像处理集成到 Faster R-CNN 架构中,并利用 VGG16 作为提取特征的骨干网络。同时,该模型在特征提取阶段后创新性地加入了高效的通道关注机制,从而显著提高了该模型在识别古代多联画中细小瑕疵时的特征描述性能。在训练过程中,利用数值模拟来增强红外热图像数据集,确保了后续实验样本测试的准确性。实证结果表明,与原始的 Faster R-CNN 模型相比,该模型的检测性能有了大幅提高,union = 0.5 以上交叉点的平均精度提高到 87.3%,小物体的平均精度提高到 54.8%。这些结果凸显了该模型的实用性和有效性,标志着缺陷检测能力取得了重大进步,为文化遗产的持续保护提供了有力的技术保障,也为今后的研究提供了方向。
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来源期刊
Heritage Science
Heritage Science Arts and Humanities-Conservation
CiteScore
4.00
自引率
20.00%
发文量
183
审稿时长
19 weeks
期刊介绍: Heritage Science is an open access journal publishing original peer-reviewed research covering: Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance. Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies. Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers. Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance. Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance. Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects. Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above. Description of novel technologies that can assist in the understanding of cultural heritage.
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