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

arXiv: https://arxiv.org/abs/2208.13054 · HTML: https://mlsanity.com/paper/2208.13054/

## 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 (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Optimized Deep Encoder-Decoder Methods for Crack Segmentation](https://mlsanity.com/paper/2008.06266/) (2020)

## Built on by (most cited first, 22 of 22)

- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections](https://mlsanity.com/paper/2403.03063/) (2024)
- [Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation](https://mlsanity.com/paper/2307.00270/) (2023)
- [Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure](https://mlsanity.com/paper/2409.02866/) (2024)
- [dacl10k: Benchmark for Semantic Bridge Damage Segmentation](https://mlsanity.com/paper/2309.00460/) (2023)
- [Topology-aware Mamba for Crack Segmentation in Structures](https://mlsanity.com/paper/2410.19894/) (2024)
- [From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring](https://mlsanity.com/paper/2309.11267/) (2023)
- [BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning](https://mlsanity.com/paper/2506.17892/) (2025)
- [ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection](https://mlsanity.com/paper/2401.03298/) (2024)
- [Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets](https://mlsanity.com/paper/2508.10256/) (2025)
- [dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Test](https://mlsanity.com/paper/2309.03763/) (2023)
- [CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures](https://mlsanity.com/paper/2412.15637/) (2024)
- [synth-dacl: Does Synthetic Defect Data Enhance Segmentation Accuracy and Robustness for Real-World Bridge Inspections?](https://mlsanity.com/paper/2506.14255/) (2025)
- [Determination Of Structural Cracks Using Deep Learning Frameworks](https://mlsanity.com/paper/2507.02416/) (2025)
- [TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels](https://mlsanity.com/paper/2512.14477/) (2025)
- [Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment](https://mlsanity.com/paper/2604.08212/) (2026)
- [UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation](https://mlsanity.com/paper/2605.02380/) (2026)
- [SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation](https://mlsanity.com/paper/2606.24935/) (2026)
- [Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees](https://mlsanity.com/paper/2607.25367/) (2026)
- [Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentation](https://mlsanity.com/paper/2608.12252/) (2026)
- [CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentation](https://mlsanity.com/paper/2609.05816/) (2026)
- [An End-to-End Automated Pipeline for Controllable Crack Data Synthesis](https://mlsanity.com/paper/2609.12431/) (2026)

## 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.