Skip to main content

Research Repository

Advanced Search

Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning

Katsigiannis, Stamos; Seyedzadeh, Saleh; Agapiou, Andrew; Ramzan, Naeem

Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning Thumbnail


Authors

Saleh Seyedzadeh

Andrew Agapiou

Naeem Ramzan



Abstract

Crack detection in masonry façades is a crucial task for ensuring the safety and longevity of buildings. However, traditional methods are often time-consuming, expensive, and labour-intensive. In recent years, deep learning techniques have been applied to detect cracks in masonry images, but these models often require large amounts of annotated data to achieve high accuracy, which can be difficult to obtain. In this article, we propose a deep learning approach for crack detection on brickwork masonry façades using transfer learning with limited annotated data. Our approach uses a pre-trained deep convolutional neural network model as a feature extractor, which is then optimised specifically for crack detection. To evaluate the effectiveness of our proposed method, we created and curated a dataset of 700 brickwork masonry façade images, and used 500 images for training, 100 for validation, and the remaining 100 images for testing. Results showed that our approach is very effective in detecting cracks, achieving an accuracy and F1-score of up to 100% when following end-to-end training of the neural network, thus being a promising solution for building inspection and maintenance, particularly in situations where annotated data is limited. Moreover, the transfer learning approach can be easily adapted to different types of masonry façades, making it a versatile tool for building inspection and maintenance.

Citation

Katsigiannis, S., Seyedzadeh, S., Agapiou, A., & Ramzan, N. (2023). Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning. Journal of Building Engineering, 76, Article 107105. https://doi.org/10.1016/j.jobe.2023.107105

Journal Article Type Article
Acceptance Date Jun 13, 2023
Online Publication Date Jul 1, 2023
Publication Date Oct 1, 2023
Deposit Date Jun 14, 2023
Publicly Available Date Jul 3, 2023
Journal Journal of Building Engineering
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 76
Article Number 107105
DOI https://doi.org/10.1016/j.jobe.2023.107105
Public URL https://durham-repository.worktribe.com/output/1171652

Files






You might also like



Downloadable Citations