Automatic damage detection and localization of ancient city walls—a case study of the Great Wall
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| Publicado en: | Heritage Science vol. 13, no. 1 (Dec 2025), p. 174 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2050-7445 | ||
| 024 | 7 | |a 10.1038/s40494-025-01749-0 |2 doi | |
| 035 | |a 3205537245 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 084 | |a 243040 |2 nlm | ||
| 245 | 1 | |a Automatic damage detection and localization of ancient city walls—a case study of the Great Wall | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Case Study Journal Article | ||
| 520 | 3 | |a Prolonged exposure to natural factors and human activities has caused severe brick missing damages in many Great Wall defensive forts, weakening their load-bearing structures. Given the Great Wall’s vast scale, remote locations, and complex terrain, there is an urgent need for a method to quickly identify and locate such damages to support daily monitoring and maintenance. This study proposes a computer vision-based two-phase automatic damage detection and localization method for ancient city walls. In phase one, an Improved-YOLOv5n object detection network, trained on 1197 UAV images, integrates attention mechanisms and knowledge distillation to enhance small target detection, achieving a mean average precision of 74.5%. In phase two, a genetic algorithm-optimized multi-threshold OTSU segmentation and image processing are used to localize damages and extract edge locations, aiding subsequent modeling. The findings of this study can provide a time-efficient, high-accuracy and non-destructive solution for routine structural safety assessments of ancient city walls. | |
| 651 | 4 | |a China | |
| 651 | 4 | |a Great Wall-China | |
| 653 | |a Localization method | ||
| 653 | |a Damage detection | ||
| 653 | |a Computer vision | ||
| 653 | |a Load bearing elements | ||
| 653 | |a Structural safety | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Damage localization | ||
| 653 | |a Object recognition | ||
| 653 | |a Image segmentation | ||
| 653 | |a Image processing | ||
| 653 | |a Walls | ||
| 653 | |a Target detection | ||
| 653 | |a Accuracy | ||
| 653 | |a Masonry | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Architecture | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Automation | ||
| 653 | |a Cracks | ||
| 653 | |a Localization | ||
| 653 | |a Case studies | ||
| 773 | 0 | |t Heritage Science |g vol. 13, no. 1 (Dec 2025), p. 174 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3205537245/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3205537245/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |