Application of Segment Anything Model for Civil Infrastructure Defect
Assessment
- URL: http://arxiv.org/abs/2304.12600v1
- Date: Tue, 25 Apr 2023 06:17:44 GMT
- Title: Application of Segment Anything Model for Civil Infrastructure Defect
Assessment
- Authors: Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Abbas Sharifi, Ali Tarlani
Beris, Mohammadsadegh Nouri, Amir Sharifzadeh Javidi
- Abstract summary: This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures.
The results indicate that each model has its own strengths and limitations for detecting different types of cracks.
- Score: 0.2936007114555107
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This research assesses the performance of two deep learning models, SAM and
U-Net, for detecting cracks in concrete structures. The results indicate that
each model has its own strengths and limitations for detecting different types
of cracks. Using the SAM's unique crack detection approach, the image is
divided into various parts that identify the location of the crack, making it
more effective at detecting longitudinal cracks. On the other hand, the U-Net
model can identify positive label pixels to accurately detect the size and
location of spalling cracks. By combining both models, more accurate and
comprehensive crack detection results can be achieved. The importance of using
advanced technologies for crack detection in ensuring the safety and longevity
of concrete structures cannot be overstated. This research can have significant
implications for civil engineering, as the SAM and U-Net model can be used for
a variety of concrete structures, including bridges, buildings, and roads,
improving the accuracy and efficiency of crack detection and saving time and
resources in maintenance and repair. In conclusion, the SAM and U-Net model
presented in this study offer promising solutions for detecting cracks in
concrete structures and leveraging the strengths of both models that can lead
to more accurate and comprehensive results.
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