mlsanityData to 14 Oct 2026

CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks

arXiv 2208.13054 ↗First posted 27 Aug 2022cs.CVcited by 22

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Abstract (arXiv)

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 27 Aug 2022.

Builds on

12 of 49 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  8. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  10. 2019Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detectioncited by 80
  11. 2019Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholdingcited by 33
  12. 2020Optimized Deep Encoder-Decoder Methods for Crack Segmentationcited by 10

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Cited by year (2026 to 14 Oct)

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

22 papers.

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  1. 2023Fine-tuning vision foundation model for crack segmentation in civil infrastructurescited by 14
  2. 2024CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspectionscited by 12
  3. 2023Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentationcited by 10
  4. 2024Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructurecited by 7
  5. 2023dacl10k: Benchmark for Semantic Bridge Damage Segmentationcited by 6
  6. 2024Topology-aware Mamba for Crack Segmentation in Structurescited by 6
  7. 2023From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoringcited by 4
  8. 2025BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learningcited by 2
  9. 2024ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detectioncited by 1
  10. 2025Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasetscited by 1
  11. 2023dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Testcited by 0
  12. 2024CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structurescited by 0
10 more
  1. 2025synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections?cited by 0
  2. 2025Determination Of Structural Cracks Using Deep Learning Frameworkscited by 0
  3. 2025TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnelscited by 0
  4. 2026Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessmentcited by 0
  5. 2026UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentationcited by 0
  6. 2026SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentationcited by 0
  7. 2026Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Leveescited by 0
  8. 2026Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentationcited by 0
  9. 2026CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentationcited by 0
  10. 2026An End-to-End Automated Pipeline for Controllable Crack Data Synthesiscited by 0