# Application of Segment Anything Model for Civil Infrastructure Defect Assessment

arXiv: https://arxiv.org/abs/2304.12600 · HTML: https://mlsanity.com/paper/2304.12600/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 73 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [Multispecies fruit flower detection using a refined semantic segmentation network](https://mlsanity.com/paper/1809.10080/) (2018)
- [Does Adam optimizer keep close to the optimal point?](https://mlsanity.com/paper/1911.00289/) (2019)

## Built on by (most cited first, 27 of 27)

- [VRP-SAM: SAM with Visual Reference Prompt](https://mlsanity.com/paper/2402.17726/) (2024)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023)
- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024)
- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [SAM-based instance segmentation models for the automation of structural damage detection](https://mlsanity.com/paper/2401.15266/) (2024)
- [SAM-SP: Self-Prompting Makes SAM Great Again](https://mlsanity.com/paper/2408.12364/) (2024)
- [SAM for Poultry Science](https://mlsanity.com/paper/2305.10254/) (2023)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models](https://mlsanity.com/paper/2307.12018/) (2023)
- [Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines](https://mlsanity.com/paper/2606.07953/) (2026)
- [Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline](https://mlsanity.com/paper/2606.07965/) (2026)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security](https://mlsanity.com/paper/2503.00331/) (2025)
- [An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything](https://mlsanity.com/paper/2312.04063/) (2023)
- [ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation](https://mlsanity.com/paper/2401.02326/) (2024)
- [3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data](https://mlsanity.com/paper/2401.06946/) (2024)
- [Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model](https://mlsanity.com/paper/2408.11253/) (2024)
- [CNN-based Labelled Crack Detection for Image Annotation](https://mlsanity.com/paper/2408.11250/) (2024)
- [Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach](https://mlsanity.com/paper/2408.12838/) (2024)
- [COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach](https://mlsanity.com/paper/2408.12841/) (2024)
- [SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model](https://mlsanity.com/paper/2410.01473/) (2024)
- [Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance](https://mlsanity.com/paper/2505.09123/) (2025)
- [Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management](https://mlsanity.com/paper/2606.08314/) (2026)
- [Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees](https://mlsanity.com/paper/2607.25367/) (2026)
- [SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentation](https://mlsanity.com/paper/2609.00469/) (2026)

## Abstract (arXiv)

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.