Crack Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

26 shown of 26 papers with code (63 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• U-Net: Convolutional Networks for Biomedical Image Segmentation
  18 May 2015 487 repositories listed Syntology ran 510 of 757 samples · 247 unverified · 426 pointer-only (licence)
  There is large consent that successful training of deep networks requires many thousand annotated training samples.
• Fully Convolutional Networks for Semantic Segmentation
  14 Nov 2014 51 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Convolutional networks are powerful visual models that yield hierarchies of features.
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
  31 May 2021 28 repositories listed Syntology ran 48 of 86 samples · 38 unverified · 15 pointer-only (licence)
  We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
• CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks
  27 Aug 2022 2 repositories listed
  We also present a pipeline that combines Image Processing and Deep Learning models.
• SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
  3 Mar 2025 1 repository listed
  The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic…
• Topology-aware Mamba for Crack Segmentation in Structures
  25 Oct 2024 1 repository listed
  CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure.
• Distribution-aware Noisy-label Crack Segmentation
  12 Oct 2024 1 repository listed
  To our knowledge, this is the first approach that effectively minimizes the adverse effects of noisy labels on the supervised learning of the SAM-Adapter.
• CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
  10 Oct 2024 1 repository listed
  Our experimental evaluation on the three-class crack image segmentation tasks within the FIND dataset demonstrates that CrackSegDiff outperforms state-of-the-art methods, particularly excelling in the detection of…
• IRFusionFormer: Enhancing Pavement Crack Segmentation with RGB-T Fusion and Topological-Based Loss
  30 Sep 2024 1 repository listed
  To address this, we propose IRFusionFormer, a novel model for crack segmentation that effectively integrates RGB and thermal data.
• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
  4 Sep 2024 1 repository listed
  Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance.
• Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation
  23 Aug 2024 1 repository listed
  The F1 and mIoU scores on the TUT dataset are 0.
• Mamba meets crack segmentation
  22 Jul 2024 1 repository listed
  Second, the proposed innovative Mamba design concept, integrating Mamba with the attention mechanism, holds significant reference value for all Mamba-based computer vision models, not limited to crack segmentation…
• Segmentation tool for images of cracks
  28 Mar 2024 1 repository listed
  Machine learning algorithms can be used for augmenting the capability of classical visual inspection of bridge structures, however, the implementation of such an algorithm requires a massive annotated training dataset,…
• CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
  5 Mar 2024 1 repository listed
  LCSD consists of 102 well-illuminated crack images and 41 low-light crack images.
• UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
  28 Jan 2024 1 repository listed
  Subsequently, a generative adversarial network is trained to restore the corrupted regions by leveraging the semantic context learned from surrounding uncorrupted regions.
• Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
  1 Jul 2023 1 repository listed
  Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.
• Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset
  15 Jun 2023 1 repository listed
  Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern.
• Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
  24 Feb 2023 1 repository listed
  This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks.
• A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness
  23 Feb 2023 1 repository listed
  And the BAM learns the boundary features from the dilated crack label.
• Learning-Based Defect Recognitions for Autonomous UAV Inspections
  13 Feb 2023 1 repository listed
  We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the…
• Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
  25 Feb 2022 1 repository listed
  This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.
• Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding
  1 Sep 2021 1 repository listed
  Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels.
• Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
  25 Jul 2021 1 repository listed
  This paper proposes a method for crack segmentation on low-resolution images.
• CrackFormer: Transformer Network for Fine-Grained Crack Detection
  1 Jan 2021 1 repository listed
  The CrackFormer is composed of novel attention modules in a SegNet-like encoder-decoder architecture.
• Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
  4 Nov 2020 1 repository listed
  Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually.
• Dual Super-Resolution Learning for Semantic Segmentation
  1 Jun 2020 1 repository listed
  Specifically, for semantic segmentation on CityScapes, we can achieve \geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.