mlsanityData to 7 Oct 2026

Application of Segment Anything Model for Civil Infrastructure Defect Assessment

arXiv 2304.12600 ↗First posted 25 Apr 2023cs.CV eess.IVcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 13 of its 73 references that are in the map. First posted on arXiv 25 Apr 2023.

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13 of 73 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2023Segment Anythingcited by 8,158
  6. 2020Scaling Laws for Neural Language Modelscited by 6,411
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  8. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  9. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  10. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  11. 2016The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentationcited by 122
  12. 2018Multispecies fruit flower detection using a refined semantic segmentation networkcited by 9
1 more
  1. 2019Does Adam optimizer keep close to the optimal point?cited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 13; 2025: 2; 2026: 5.

27 papers.

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  1. 2024VRP-SAM: SAM with Visual Reference Promptcited by 75
  2. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  3. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  4. 2024Towards Generalizable Scene Change Detectioncited by 17
  5. 2024On Efficient Variants of Segment Anything Model: A Surveycited by 16
  6. 2023Fine-tuning vision foundation model for crack segmentation in civil infrastructurescited by 14
  7. 2024SAM-based instance segmentation models for the automation of structural damage detectioncited by 10
  8. 2024SAM-SP: Self-Prompting Makes SAM Great Againcited by 8
  9. 2023SAM for Poultry Sciencecited by 6
  10. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  11. 2023GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Modelscited by 3
  12. 2026Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselinescited by 3
15 more
  1. 2026Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baselinecited by 3
  2. 2024Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchaincited by 2
  3. 2025PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Securitycited by 2
  4. 2023An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anythingcited by 1
  5. 2024ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentationcited by 1
  6. 20243D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Datacited by 1
  7. 2024Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Modelcited by 1
  8. 2024CNN-based Labelled Crack Detection for Image Annotationcited by 0
  9. 2024Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approachcited by 0
  10. 2024COVID-19 Probability Prediction Using Machine Learning: An Infectious Approachcited by 0
  11. 2024SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Modelcited by 0
  12. 2025Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidancecited by 0
  13. 2026Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Managementcited by 0
  14. 2026Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Leveescited by 0
  15. 2026SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentationcited by 0