Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2517 |
| _version_ | 1797598486769696768 |
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| author | Hyungjoon Seo Aishwarya Deepak Raut Cheng Chen Cheng Zhang |
| author_facet | Hyungjoon Seo Aishwarya Deepak Raut Cheng Chen Cheng Zhang |
| author_sort | Hyungjoon Seo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:22:48Z |
| format | Article |
| id | doaj.art-c8740fd7e3554a648ca822cc65ed0de6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:22:48Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c8740fd7e3554a648ca822cc65ed0de62023-11-18T03:06:14ZengMDPI AGRemote Sensing2072-42922023-05-011510251710.3390/rs15102517Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal ImagingHyungjoon Seo0Aishwarya Deepak Raut1Cheng Chen2Cheng Zhang3Department of Civil Engineering and Industrial Design, University of Liverpool, Liverpool L69 7WW, UKDepartment of Civil Engineering and Industrial Design, University of Liverpool, Liverpool L69 7WW, UKDepartment of Civil Engineering, Xi’an Jiaotong-Liverpool University, 111 Renai Road, Dushu Lake, Suzhou 215000, ChinaDepartment of Civil Engineering, Xi’an Jiaotong-Liverpool University, 111 Renai Road, Dushu Lake, Suzhou 215000, ChinaIn 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.https://www.mdpi.com/2072-4292/15/10/2517multi-label classificationautomatic damage detectionheritage buildingCNNinfrared thermal imaging |
| spellingShingle | Hyungjoon Seo Aishwarya Deepak Raut Cheng Chen Cheng Zhang Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging Remote Sensing multi-label classification automatic damage detection heritage building CNN infrared thermal imaging |
| title | Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging |
| title_full | Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging |
| title_fullStr | Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging |
| title_full_unstemmed | Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging |
| title_short | Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging |
| title_sort | multi label classification and automatic damage detection of masonry heritage building through cnn analysis of infrared thermal imaging |
| topic | multi-label classification automatic damage detection heritage building CNN infrared thermal imaging |
| url | https://www.mdpi.com/2072-4292/15/10/2517 |
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