Automatic damage detection and localization of ancient city walls—a case study of the Great Wall

Abstract

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.