Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review

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@article{91d01982,
title = {Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review},
journal = {Journal of Cultural Heritage},
volume = {66},
pages = {536-550},
year = {2024},
issn = {1296-2074},
doi = {https://doi.org/10.1016/j.culher.2024.01.005},
url = {https://www.sciencedirect.com/science/article/pii/S1296207424000050},
author = {Mayank Mishra and Paulo B. Lourenço},
keywords = {Deep learning, UAV, Damage identification, Convolutional neural networks, Computer vision, Structural health monitoring},
abstract = {Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reported damage detection in concrete and bridge structures using AI techniques. However, few studies have quantified defects in CH structures using the AI paradigm, and limited case studies exist for their applications. Hence, the application of AI-assisted visual inspections for CH sites needs to be explored. AI-assisted digital inspections assist inspection professionals and increase confidence levels in the damage assessment of CH buildings. This review summarizes the damage assessment techniques using image processing techniques, focusing mainly on DL techniques applied for CH conservation. Several case study applications of CH buildings are presented where AI can assist in traditional visual inspections.}
}