Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approac...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858133/ |
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| author | Mehmet Yilmaz Erkut Yalcin Fatih Demir Ahmet Munir Ozdemir Muhammed Atar Aysegul Gunes Beyza Furtana Yalcin Ertugrul Cambay |
| author_facet | Mehmet Yilmaz Erkut Yalcin Fatih Demir Ahmet Munir Ozdemir Muhammed Atar Aysegul Gunes Beyza Furtana Yalcin Ertugrul Cambay |
| author_sort | Mehmet Yilmaz |
| collection | DOAJ |
| description | This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions. |
| format | Article |
| id | doaj-art-fa55c2f6b2ea4abf953307ffd6e2ca87 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fa55c2f6b2ea4abf953307ffd6e2ca872025-02-07T00:01:41ZengIEEEIEEE Access2169-35362025-01-0113228202283010.1109/ACCESS.2025.353655410858133Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based ApproachesMehmet Yilmaz0Erkut Yalcin1https://orcid.org/0000-0002-6389-4211Fatih Demir2https://orcid.org/0000-0003-3210-3664Ahmet Munir Ozdemir3https://orcid.org/0000-0002-4872-154XMuhammed Atar4Aysegul Gunes5https://orcid.org/0009-0008-5240-1267Beyza Furtana Yalcin6Ertugrul Cambay7Department of Civil Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Civil Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Software Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Civil Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Bursa, TürkiyeDepartment of Civil Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Civil Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Civil Engineering, Faculty of Engineering, Firat University, Elâzığ, TürkiyeDepartment of Civil Engineering, Faculty of Engineering, Bitlis Eren University, Bitlis, TürkiyeThis study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.https://ieeexplore.ieee.org/document/10858133/Asphalt deformationsdecision support systemsdeep learningsegmentation |
| spellingShingle | Mehmet Yilmaz Erkut Yalcin Fatih Demir Ahmet Munir Ozdemir Muhammed Atar Aysegul Gunes Beyza Furtana Yalcin Ertugrul Cambay Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches IEEE Access Asphalt deformations decision support systems deep learning segmentation |
| title | Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches |
| title_full | Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches |
| title_fullStr | Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches |
| title_full_unstemmed | Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches |
| title_short | Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches |
| title_sort | automatic segmentation of asphalt cracks on highways after large scale and severe earthquakes using deep learning based approaches |
| topic | Asphalt deformations decision support systems deep learning segmentation |
| url | https://ieeexplore.ieee.org/document/10858133/ |
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