Prediction and measurement of damage to architectural heritages facades using convolutional neural networks
This paper set out an automatic multicategory damage detection technique using convolutional neural networks (CNN) models based on image classification and features’ extraction, to detect damages of historic structures such as: erosion, material loss, color change of the stone, and sabotage issues....
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| Veröffentlicht in: | Neural computing & applications 2022-10, Vol.34 (20), p.18125-18141 |
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| container_title | Neural computing & applications |
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| creator | Samhouri, Murad Al-Arabiat, Lujain Al-Atrash, Farah |
| description | This paper set out an automatic multicategory damage detection technique using convolutional neural networks (CNN) models based on image classification and features’ extraction, to detect damages of historic structures such as: erosion, material loss, color change of the stone, and sabotage issues. The city of “Al-Salt” in Jordan was selected for the case study in this research. The best model showed an average damage detection accuracy of 95%. It was demonstrated that the proposed CNN model was significantly powerful, effective and reliable for damage detection of historic masonry buildings using features’ extraction based on imaging, and it contributed to the management and safety of historic heritage and preservation. |
| doi_str_mv | 10.1007/s00521-022-07461-5 |
| format | Article |
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| subjects | Architecture Artificial Intelligence Artificial neural networks Buildings Case studies Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Cultural heritage Damage detection Data Mining and Knowledge Discovery Deep learning Feature extraction Historical buildings Historical structures Image classification Image Processing and Computer Vision Neural networks Original Article Preventive maintenance Probability and Statistics in Computer Science Sabotage Safety management Support vector machines |
| title | Prediction and measurement of damage to architectural heritages facades using convolutional neural networks |
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