Surface Damage Detection Of Ancient Buildings Based On Multi-View 3D Reconstruction

Chinese ancient architecture has gone through thousands of years of evolution and development,forming a unique architectural style that stands out in the eastern world.It is on par with European architecture and Islamic architecture,and has become one of the world’s three major architectural systems.These buildings carry the wisdom of the Chinese nation and have extremely high historical and cultural research value.Therefore,the restoration and protection of ancient buildings is a complex project involving multiple fields and aspects of knowledge.Damage detection is a crucial part of ancient building protection.There are three main types of damage to ancient buildings: surface damage,internal damage,and foundation damage.Surface damage is the most obvious form of damage,including cracking,peeling,and corrosion.Currently,there is little research on the detection of cracks in ancient buildings,and traditional methods rely on manual visual inspection to detect damage to cultural relics.However,this approach is inefficient,risky,and difficult,as there are areas that cannot be accessed,the results have a large margin of error,and specialized personnel are required.The detection cycle is also long,making it difficult to efficiently provide feedback on damage to ancient buildings,and can easily cause damage to the struct ures themselves.With the increasing attention to computer vision-based crack detection methods,emerging technologies such as drones and artificial intelligence have been developed for intelligent detection of ancient building surfaces in recent years.Th ese methods have overcome the shortcomings of traditional detection methods in terms of efficiency and safety,and significantly improved the digital and intelligent level of ancient building appearance detection.Intelligent detection methods for ancient buildings have become a hot research topic.However,traditional digital image technology is susceptible to factors such as diverse crack morphology and significant environmental noise,making it difficult to meet engineering requirements.This paper uses a deep learning-based damage detection algorithm to detect damage to ancient towers.However,two-dimensional digital images can only provide local information about damage to ancient buildings,making it difficult to quickly locate the position of a large number of damages.To address this problem,this paper introduces a three-dimensional reconstruction algorithm to construct an integrated and composite imaging model that combines two-dimensional damage image mapping to locate the damage.The main research content of this paper includes the following aspects:(1)This text describes a research project that involves using a multi-view image3 D reconstruction algorithm to improve upon traditional 3D reconstruction methods.Specifically,the SIFT algorithm used for feature extraction and matching in the traditional approach has been replaced with the AKAZE algorithm.(2)Using a deep learning-based damage detection algorithm,specifically YOLOv7,to recognize damage to ancient buildings.Using a deep learning image segmentation algorithm based on Unet3+ to segment the damage in images.(3)Combining YOLOv7 with Unet3+ for damage detection and segmentation,obtaining 2D images after damage recognition,and using multi-angle 3D reconstruction algorithms such as sparse reconstruction,dense reconstruction,and surface reconstruction to locate the damage in ancient buildings.