Detection of Historical Building Crack Using CDNet Model

Priya Mittal, Bhisham Sharma, Komuravelly Sudheer Kumar, Haeedir Mohameed, Haeedir Mohameed

2025

Abstract

It is crucial to address the cracks in ancient monuments to preserve and safeguard them in alignment with the sustainable development goals (SDG). Thus, a new deep learning-based innovative CNN model, CDNet, is proposed for crack detection, which overcomes the challenges of manual detection. Our model was trained and evaluated using the Historical Crack Dataset. It achieved outstanding results, with 99% accuracy, 98.99% precision, and 99.01% recall. These results beat the performance of both the VGG16 and ResNet-50 models. The CDNet model can be subsequently deployed to the cloud for real-time applications.

Paper Citation

in Harvard Style

Mittal P., Sharma B., Kumar K. and Mohameed H. (2025). Detection of Historical Building Crack Using CDNet Model. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 438-443. DOI: 10.5220/0013621200004664

in Bibtex Style

@conference{incoft25,
author={Priya Mittal and Bhisham Sharma and Komuravelly Sudheer Kumar and Haeedir Mohameed},
title={Detection of Historical Building Crack Using CDNet Model},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={438-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013621200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Detection of Historical Building Crack Using CDNet Model
SN - 978-989-758-763-4
AU - Mittal P.
AU - Sharma B.
AU - Kumar K.
AU - Mohameed H.
PY - 2025
SP - 438
EP - 443
DO - 10.5220/0013621200004664
PB - SciTePress