Seo, Hyungjoon, Raut, Aishwarya Deepak, Chen, Cheng and Zhang, Cheng 
ORCID: 0000-0003-2465-1767
(2023)
Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging.
REMOTE SENSING, 15 (10).
p. 2517.
Abstract
<jats:p>In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm.</jats:p>
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | automatic damage detection, CNN, heritage building, infrared thermal imaging, multi-label classification |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Jun 2023 09:08 |
| Last Modified: | 15 Mar 2024 14:56 |
| DOI: | 10.3390/rs15102517 |
| Open Access URL: | https://doi.org/10.3390/rs15102517 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171175 |
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