Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage
Assessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both cl...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8260 |
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| author | Laya Targa Carmen Cano Álvaro Solbes-García Sergio Casas Ester Alba Cristina Portalés |
| author_facet | Laya Targa Carmen Cano Álvaro Solbes-García Sergio Casas Ester Alba Cristina Portalés |
| author_sort | Laya Targa |
| collection | DOAJ |
| description | Assessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both classical image processing techniques (Canny, Sobel, and Laplacian) and a deep learning model (DexiNed). The methodology integrates interdisciplinary collaboration between conservation professionals and computer scientists, applying these algorithms to artworks affected by environmental damage, including flooding. Preprocessing and post-processing techniques were used to enhance detection accuracy and reduce noise. The results show that while traditional methods often yield higher precision and recall scores, they are also sensitive to texture and contrast variations. These findings suggest that automated edge detection can support conservation efforts by streamlining condition assessments and improving documentation. |
| format | Article |
| id | doaj-art-8fbdf2b5252c4af6b3707a57afde0b31 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8fbdf2b5252c4af6b3707a57afde0b312025-08-20T04:00:50ZengMDPI AGApplied Sciences2076-34172025-07-011515826010.3390/app15158260Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster DamageLaya Targa0Carmen Cano1Álvaro Solbes-García2Sergio Casas3Ester Alba4Cristina Portalés5Institute of Robotics and Information and Communication Technologies (IRTIC), Universitat de València, 46980 Valencia, SpainDepartment of Art History, Universitat de València, 46010 Valencia, SpainDepartment of Art History, Universitat de València, 46010 Valencia, SpainInstitute of Robotics and Information and Communication Technologies (IRTIC), Universitat de València, 46980 Valencia, SpainDepartment of Art History, Universitat de València, 46010 Valencia, SpainInstitute of Robotics and Information and Communication Technologies (IRTIC), Universitat de València, 46980 Valencia, SpainAssessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both classical image processing techniques (Canny, Sobel, and Laplacian) and a deep learning model (DexiNed). The methodology integrates interdisciplinary collaboration between conservation professionals and computer scientists, applying these algorithms to artworks affected by environmental damage, including flooding. Preprocessing and post-processing techniques were used to enhance detection accuracy and reduce noise. The results show that while traditional methods often yield higher precision and recall scores, they are also sensitive to texture and contrast variations. These findings suggest that automated edge detection can support conservation efforts by streamlining condition assessments and improving documentation.https://www.mdpi.com/2076-3417/15/15/8260cultural heritage conservationedge detectionimage processingdamage mapping |
| spellingShingle | Laya Targa Carmen Cano Álvaro Solbes-García Sergio Casas Ester Alba Cristina Portalés Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage Applied Sciences cultural heritage conservation edge detection image processing damage mapping |
| title | Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage |
| title_full | Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage |
| title_fullStr | Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage |
| title_full_unstemmed | Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage |
| title_short | Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage |
| title_sort | automated edge detection for cultural heritage conservation comparative evaluation of classical and deep learning methods on artworks affected by natural disaster damage |
| topic | cultural heritage conservation edge detection image processing damage mapping |
| url | https://www.mdpi.com/2076-3417/15/15/8260 |
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