Protecting Historical Treasures: Deep Learning for Structural Health Assessment

  • Authors

    • M. Snehapriya Research Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India
    • A. Umamageswari Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India
    https://doi.org/10.14419/zc5xbc77

    Received date: July 27, 2025

    Accepted date: September 9, 2025

    Published date: September 25, 2025

  • Single Shot Multibox Detector (SSD), Cracks, Moss, Seepages

  • Abstract

    Preserving ancient monuments not only stands as evidence of the collective history but is also a fundamental aspect of conserving cultural heritage. As these architectural wonders age, they become susceptible to the encroachment of moss and the development of structural cracks, jeopardizing their overall integrity. In the present era of advanced technology, the use of deep learning algorithms for the automatic detection and classification of cracks, moss, and seepages arises as a promising solution. This study explores the deployment of deep learning techniques, particularly Single Shot Multibox Detector (SSD), for the identification and localization of moss and cracks on ancient monuments. The proposed work investigates the effectiveness of these algorithms in analyzing high-resolution images, offering a non-invasive and precise means of monitoring structural defects and biological growth with the help of the dataset Indian Ancient Monuments (Kaggle). To delve into the architectural significance of these structures, which underlines the importance of their preservation. The deep learning models, trained on the dataset, exhibit remarkable proficiency in distinguishing between normal surfaces and those bearing cracks, moss, and seepages. We assess the model’s performance through metrics such as accuracy, precision, recall, and F1-score. The proposed work is compared with the state-of-the-art techniques like SVM, KNN, CNN, and Random Forest in the field. Furthermore, this study provides a glimpse into potential applications of the developed models, including real-time monitoring and alert systems for heritage conservationists and preservation authorities. By amalgamating advanced deep learning technology SSD with heritage preservation, this research underscores a proactive approach to safeguarding ancient monuments with the accuracy of detecting cracks, moss, and seepages as 96.9%, 97.5% and 97.5% respectively. The implications extend beyond the technical realm, transcending into cultural heritage and conservation practices, thereby ensuring the longevity of these historical treasures.

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  • How to Cite

    Snehapriya , M., & Umamageswari , A. (2025). Protecting Historical Treasures: Deep Learning for Structural Health Assessment. International Journal of Basic and Applied Sciences, 14(5), 840-852. https://doi.org/10.14419/zc5xbc77