Browsing by Author "Hu, Jue"

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    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
    (Springer, 2024-09-16) Wang, Xin; Jiang, Guimin; Hu, Jue; Sfarra, Stefano; Mostacci, Miranda; Kouis, Dimitrios; Yang, Dazhi; Fernandes, Henrique; Avdelidis, Nicolas P.; Maldague, Xavier; Gai, Yonggang; Zhang, Hai
    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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    Non-destructive imaging of marqueteries based on a new infrared-terahertz fusion technique
    (Elsevier, 2022-06-29) Hu, Jue; Zhang, Hai; Sfarra, Stefano; Gargiulo, Gianfranco; Avdelidis, Nicolas Peter; Zhang, Mingli; Yang, Dazhi; Maldague, Xavier
    Detection of subsurface defects has hitherto been regarded as an important element in the course of preserving cultural heritage. To do so, non-destructive imaging approaches for viewing and determining the location of splitting inside the sample under test are required, which constitute the subject of the present study. Both active thermography and terahertz imaging have demonstrated their potential in providing non-destructive inspection on cultural heritage objects. Conventionally, active thermography has been used to retrieve details on the defects as well as morphological data from the surface and subsurface, whereas pulsed terahertz imaging has been applied to record the internal material distribution. Here, the feature extraction, selection and fusion framework is extended to design a fusion process to merge the information obtained by both active thermography and terahertz imaging; in this way, the technique naturally inherits the strengths of both aforementioned imaging technologies. The fusion technique is able to produce images with high-contrast defect information located at different depths. To demonstrate the efficacy of the suggested technique, an experiment has been conducted on an ancient marquetry.