An Integrated Method Using a Convolutional Autoencoder, Thresholding Techniques, and a Residual Network for Anomaly Detection on Heritage Roof Surfaces

Abstract

The roofs of heritage buildings are subject to long-term degradation, resulting in poor heat insulation, heat regulation, and water leakage prevention. Researchers have predominantly employed feature-based traditional machine learning methods or individual deep learning techniques for the detection of natural deterioration and human-made damage on the surfaces of heritage building roofs for preservation. Despite their success, balancing accuracy, efficiency, timeliness, and cost remains a challenge, hindering practical application. The paper proposes an integrated method that employs a convolutional autoencoder, thresholding techniques, and a residual network to automatically detect anomalies on heritage roof surfaces. Firstly, unmanned aerial vehicles (UAVs) were employed to collect the image data of the heritage building roofs. Subsequently, an artificial intelligence (AI)-based system was developed to detect, extract, and classify anomalies on heritage roof surfaces by integrating a convolutional autoencoder, threshold techniques, and residual networks (ResNets). A heritage building project was selected as a case study. The experiments demonstrate that the proposed approach improved the detection accuracy and efficiency when compared with a single detection method. The proposed method addresses certain limitations of existing approaches, especially the reliance on extensive data labeling. It is anticipated that this approach will provide a basis for the formulation of repair schemes and timely maintenance for preventive conservation, enhancing the actual benefits of heritage building restoration.

Publication DOI: https://doi.org/10.3390/buildings14092828
Divisions: College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
Funding Information: This work was supported by the “National Natural Science Foundation of China (NNSFC)” under Grant number 72301256.
Additional Information: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: UAV,computer vision,detection and evaluation,heritage buildings,roof damage,Architecture ,Civil and Structural Engineering,Building and Construction
Publication ISSN: 2075-5309
Data Access Statement: The data that support the findings of this study are available from the<br/>corresponding author upon reasonable request.
Last Modified: 05 Dec 2024 08:24
Date Deposited: 23 Oct 2024 17:44
Full Text Link: https://www.mdp ... -5309/14/9/2828
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-09
Published Online Date: 2024-09-08
Accepted Date: 2024-09-05
Authors: Zhang, Yongcheng
Kong, Liulin
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Zhang, Qingzhi

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