Academic Journal
Detecting surface defects of heritage buildings based on deep learning
| Τίτλος: | Detecting surface defects of heritage buildings based on deep learning |
|---|---|
| Συγγραφείς: | Fu Xiaoli, Angkawisittpan Niwat |
| Πηγή: | Journal of Intelligent Systems, Vol 33, Iss 1, Pp 163-9 (2024) |
| Στοιχεία εκδότη: | De Gruyter, 2024. |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | LCC:Science LCC:Electronic computers. Computer science |
| Θεματικοί όροι: | deep learning, flipping, historical places, images, sustainability, thermography, transformer-based segmentation, Science, Electronic computers. Computer science, QA75.5-76.95 |
| Περιγραφή: | The present study examined the usage of deep convolutional neural networks (DCNNs) for the classification, segmentation, and detection of the images of surface defects in heritage buildings. A survey was conducted on the building surface defects in Gulang Island (a UNESCO World Cultural Heritage Site), which were subsequently classified into six categories according to relevant standards. A Swin Transformer- and YOLOv5-based model was built for the automated detection of surface defects. Experimental results suggested that the proposed model was 99.2% accurate at classifying plant penetration and achieved a mean intersection-over-union (mIoU) of over 92% in relation to moss, cracking, alkalization, staining, and deterioration, outperforming CNN-based semantic segmentation networks such as FCN, PSPNet, and DeepLabv3plus. The Swin Transformer-based approach for the segmentation of building surface defect images achieved the highest accuracy regardless of the evaluation metric (with an mIoU of 90.96% and an mAcc of 95.78%), when contrasted to mainstream DCNNs such as SegFormer, PSPNet, and DANet. |
| Τύπος εγγράφου: | article |
| Περιγραφή αρχείου: | electronic resource |
| Γλώσσα: | English |
| ISSN: | 2191-026X |
| Relation: | https://doaj.org/toc/2191-026X |
| DOI: | 10.1515/jisys-2023-0048 |
| Σύνδεσμος πρόσβασης: | https://doaj.org/article/0268a861afb545a0b2dc790a6ef80ecf |
| Αριθμός Καταχώρησης: | edsdoj.0268a861afb545a0b2dc790a6ef80ecf |
| Βάση Δεδομένων: | Directory of Open Access Journals |
| ISSN: | 2191026X |
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| DOI: | 10.1515/jisys-2023-0048 |