Cited by 165Infrastructure Maintenance and Monitoring3D Surveying and Cultural HeritageStructural Health Monitoring Techniques

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

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

Open access
Mishra, Mayank, Lourenço, Paulo B.·2024·Journal of Cultural Heritage

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.

Overview

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Crossref · 10.1016/j.culher.2024.01.005

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Citations

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Research works printed but not linked: 8

  1. Cardellicchio, 2023, On the use of YOLOv5 for detecting common defects on existing RC bridges, vol. 12621, 134
  2. Jüngert, 2008, Damage detection in wind turbine blades using two different acoustic techniques, NDT Database J. (NDT), 2075
  3. Ciampa, 2019, Practical issues on the use of drones for construction inspections, vol. 1249, 012016
  4. Valero, 2019, In situ assessment of superficial moisture condition in façades of historic building using non-destructive techniques, Case Stud. Constr. Mater., 10, e00228
  5. Mansuri, 2022, Artificial intelligence-based automatic visual inspection system for built heritage, Smart Sustain, Built Environ., 11, 622
  6. Ren, 2015, towards real-time object detection with region proposal networks, Adv. Neural Inform. Process. Syst., 28, 1
  7. Dini, 2023, Applying deep learning and convolutional neural network system to identity historic buildings: the “Little China” building in central java, Indonesia, 10, 187
  8. Chaiyasarn, 2021, Tile damage detection in temple façade via convolutional neural networks, J. Eng. Sci. Technol, 16, 3057

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Metadata

Authors
Mishra, M., & Lourenço, P. B.
Affiliations
University of Minho (PT)
Corresponding author
Mishra, Mayank
Venue
Journal of Cultural Heritage
Publisher
Elsevier BV
Published
2024-03
Volume
66
Pages
536-550
ISSN
1296-2074 (ISSN-L)
OpenAlex
W4391202537
Cited by
165
References
90
Citations by year
2026: 67 · 2025: 70 · 2024: 28
FWCI
27.55
Type
journal article
Language
en
Open access
Locations
  • Journal of Cultural Heritage (publishedVersion, cc-by)
  • CINECA IRIS Institutional Research Information System (University of Basilicata) (submittedVersion)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc-nd)
Funders
  • EU Framework Programme for Research and Innovation Marie Skłodowska-Curie Actions (101063722)
  • Fundação para a Ciência e a Tecnologia
  • Ministério da Ciência, Tecnologia e Ensino Superior (UIDB / 04029/2020)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Keywords
artificial intelligence, visual inspection, cultural heritage preservation, damage detection, deep learning, computer vision, weathering, structural deterioration, historic buildings, image processing, concrete structures, bridges

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