Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achie...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/8/281 |
| _version_ | 1797432555292590080 |
|---|---|
| author | Esraa Elhariri Nashwa El-Bendary Shereen A. Taie |
| author_facet | Esraa Elhariri Nashwa El-Bendary Shereen A. Taie |
| author_sort | Esraa Elhariri |
| collection | DOAJ |
| description | Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>U</mi><mn>2</mn></msup><mo>−</mo><mi>N</mi><mi>e</mi><mi>t</mi></mrow></semantics></math></inline-formula> deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces. |
| first_indexed | 2024-03-09T10:03:13Z |
| format | Article |
| id | doaj.art-304ecd0d5d544b67a3b382214dfc1ce8 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T10:03:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-304ecd0d5d544b67a3b382214dfc1ce82023-12-01T23:17:35ZengMDPI AGAlgorithms1999-48932022-08-0115828110.3390/a15080281Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net ModelsEsraa Elhariri0Nashwa El-Bendary1Shereen A. Taie2Faculty of Computers and Information, Fayoum University, Fayoum 63514, EgyptCollege of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Aswan 81516, EgyptFaculty of Computers and Information, Fayoum University, Fayoum 63514, EgyptCrack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>U</mi><mn>2</mn></msup><mo>−</mo><mi>N</mi><mi>e</mi><mi>t</mi></mrow></semantics></math></inline-formula> deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces.https://www.mdpi.com/1999-4893/15/8/281deep feature learningcrack detectionpixel-level segmentationhistorical surfacesU-Netdata augmentation |
| spellingShingle | Esraa Elhariri Nashwa El-Bendary Shereen A. Taie Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models Algorithms deep feature learning crack detection pixel-level segmentation historical surfaces U-Net data augmentation |
| title | Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models |
| title_full | Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models |
| title_fullStr | Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models |
| title_full_unstemmed | Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models |
| title_short | Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models |
| title_sort | automated pixel level deep crack segmentation on historical surfaces using u net models |
| topic | deep feature learning crack detection pixel-level segmentation historical surfaces U-Net data augmentation |
| url | https://www.mdpi.com/1999-4893/15/8/281 |
| work_keys_str_mv | AT esraaelhariri automatedpixelleveldeepcracksegmentationonhistoricalsurfacesusingunetmodels AT nashwaelbendary automatedpixelleveldeepcracksegmentationonhistoricalsurfacesusingunetmodels AT shereenataie automatedpixelleveldeepcracksegmentationonhistoricalsurfacesusingunetmodels |