Application of Segment Anything Model for Civil Infrastructure Defect Assessment

TL;DR

This study evaluates the effectiveness of SAM and U-Net deep learning models for crack detection in concrete structures, highlighting their complementary strengths and potential for improved structural health monitoring.

Contribution

It introduces a combined approach using SAM and U-Net for more accurate crack detection in civil infrastructure, demonstrating their respective advantages.

Findings

SAM is effective for longitudinal crack detection.

U-Net accurately detects crack size and location.

Combining both models improves overall detection accuracy.

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.