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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
Publicado:
Springer Nature B.V.
Materias:
China
Great Wall-China
Localization method
Damage detection
Computer vision
Load bearing elements
Structural safety
Genetic algorithms
Damage localization
Object recognition
Image segmentation
Image processing
Walls
Target detection
Accuracy
Masonry
Unmanned aerial vehicles
Architecture
Methods
Algorithms
Automation
Cracks
Localization
Case studies
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2050-7445
DOI:10.1038/s40494-025-01749-0
Fuente:Materials Science Database