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The most reliable crack-segmentation datasets are built around five decisions: define whether the model predicts crack pixels or separate crack instances, write an annotation policy before labeling, preserve exact image–mask geometry, split by source to prevent leakage, and preprocess without erasing thin cracks. Every image should have one intentional label, documented class values, and a checked overlay before it reaches training.
There is no universal mask encoding, resize setting, enhancement filter, or sample count. A masonry dataset may use white 255 for crack and black 0 for background, while another uses mask values 1 and 0. Treat those as project conventions, not standards, and make your loader match your files.
1. Decide what the model must predict
Annotation format follows the output you need at deployment. Decide this before collecting images or drawing the first mask.
Semantic segmentation: one label per pixel
Semantic segmentation assigns every pixel to a class such as crack or background. All connected cracks can share the same class, even when branches intersect. This is the usual choice when the objective is a crack map, damaged-area measurement, or a pixel-level overlay for inspection.
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Instance segmentation: one shape per crack object
Instance segmentation separates individual crack objects. It is useful when downstream software must count cracks or track each object independently. Polygon annotations are a common representation. Ultralytics’ documented Crack-Seg dataset, for example, uses one crack class with polygons. A polygon workflow and a binary raster-mask workflow are not interchangeable without a deliberate conversion and a rule for touching or crossing cracks.
| Decision | Semantic output | Instance output |
|---|---|---|
| Prediction | Crack versus background for each pixel | Separate object mask for each crack |
| Typical annotation | Binary raster mask | One polygon (or equivalent shape) per instance |
| Branch and intersection handling | Connected pixels remain the crack class | Annotators must decide whether crossing segments are one object or multiple objects |
| Main trade-off | Usually simpler and faster to label | More information, but more decisions and labeling effort |
Do not ask annotators to switch between these interpretations during a project. If you need both outputs, define how a single source annotation will be converted and review the conversion on a pilot set.
2. Write an annotation policy before drawing
A short written policy prevents the same visual situation from receiving different labels. Include the class names, pixel values, file format, boundary rule, and examples of difficult cases.
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Define classes and pixel values
For a binary semantic mask, state exactly which value means crack and which means background. One published masonry dataset uses crack pixels at 255 (white) and background at 0 (black), with the mask filename formed by appending _mask to the matching image name. The Indian Footpath Damage Segmentation dataset instead describes binary PNG masks with damaged areas at value 1 and background at 0. Both are valid if the training loader expects the same convention.
Record the convention in a README or dataset manifest, including whether the stored type is an 8-bit image, a palette image, or another format. Do not infer class meaning from how a viewer displays the file: a white-looking mask may contain 1, 255, or another value.
Set boundary rules
Specify whether annotators trace the visible outer edge of a crack, the apparent centerline expanded to its visible width, or an estimated boundary where the crack fades into the surface. Use the same rule for hairline and wide cracks. State how to handle pixels partly covered by dust, paint, vegetation, water, or debris. If a boundary cannot be decided reliably, mark the example for review under a documented project rule rather than allowing each annotator to improvise.
Resolve branches, joints, shadows, and texture
Crack imagery often contains branches, intersections, mortar or surface joints, dark shadows, holes, and textured material. Your policy should say whether a joint is background unless a crack is visibly present, and whether a shadow alone is background. Include positive and negative examples for each case. The cited sources identify these situations as important, but they do not establish one universal answer; the correct rule depends on what your deployed system must report.
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Choose the annotation tool and review method
LabelMe is one documented tool used to create manual labels in the Indian Footpath dataset. The tool is less important than the policy, versioned instructions, and review process. Have a second person label a pilot subset or adjudicate disagreements, especially where crack boundaries disappear into rough surfaces. No universal inter-annotator agreement threshold or labeling time is established by the cited datasets, so set a project-specific acceptance rule and record unresolved cases.
3. Collect images that represent deployment
A model learns the appearance you provide, not the appearance you intended. Match the collection conditions to the surfaces, cameras, distances, and lighting expected when the model is used.
Cover physical and visual variation
- Crack width: include hairline, medium, and wide examples. SDNET2018 reports widths from 0.06 mm to 25 mm.
- Contrast: include light cracks, dark cracks, partially filled cracks, and cracks obscured by debris.
- Surface: vary concrete, masonry, asphalt, coatings, rough aggregate, edges, holes, and other materials that occur in your service environment.
- Illumination: capture the expected sun, shade, artificial light, glare, and directional shadows. Shadows can resemble cracks and can also hide them.
- Viewpoint and scale: include the camera distance, angle, and field of view used in inspection. Close-up imagery does not automatically represent a road or facade survey taken from farther away.
- Texture and obstruction: include roughness, scaling, stains, dirt, and debris when they will be present at inference time.
Record source, site or route, surface type, camera or session, viewpoint, and environmental condition where practical. These fields help you form leakage-safe splits and diagnose failures later.
Use published counts as context, not targets
Specific datasets illustrate scale and diversity, but their counts are not recommended sample sizes and do not guarantee accuracy.
| Dataset or documentation | Images | Documented split | Annotation details |
|---|---|---|---|
| SDNET2018 (authors, 2018) | More than 56,000 | Not used here as a recommended split | Crack widths reported from 0.06 mm to 25 mm; examples include shadows, roughness, scaling, edges, holes, and debris |
| Ultralytics Crack Segmentation Dataset documentation (accessed 2026) | 4,029 | 3,717 training; 200 validation; 112 test | One crack class represented with polygons |
| Scientific Data dataset (2025) | 583 | 411 training; 85 validation; 87 test | Manual LabelMe annotation with paired JPG images and binary PNG masks |
A separate manually annotated repository describes thin, medium, and thick cracks, variable image resolution, and substantial texture variation. Use such descriptions to test whether your own collection contains the hard cases your deployment will encounter.
4. Preserve every image–mask pair and its geometry
Training depends on exact correspondence: the pixel at row r, column c in the mask must describe the same location in the image. A visually plausible mask shifted by a few pixels can teach the model the wrong boundary.
Use a deterministic naming rule
Choose one-to-one names such as scene_0042.jpg and scene_0042_mask.png, or another documented convention. The important properties are uniqueness, predictable pairing, and no accidental reuse of an old mask. Keep an inventory that records the original filename, mask filename, dimensions, orientation, class values, source identity, and annotation status.
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Check dimensions and orientation
- Verify that each intended image has exactly one mask and that every mask has an intended image.
- Compare width, height, and orientation metadata before training.
- Open overlays, not just the two files separately. A mask can have matching dimensions yet be displaced by an incorrect crop, rotation, or EXIF orientation.
- Check that image and mask transformations use the same coordinate system.
Crop large images without losing provenance
Very high-resolution images are often cropped into training patches. Apply identical crop coordinates to the image and mask, record the parent image and crop rectangle, and retain the parent identity in your manifest. If a crack crosses a tile boundary, use overlapping tiles or another documented border policy so the model does not see systematically truncated examples. Never place two nearby patches from one original scene into different dataset partitions.
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5. Split the data before tuning the model
Create training, validation, and test partitions before selecting preprocessing, thresholds, or model settings. Otherwise, decisions can quietly adapt to the test set.
Split by source, not by filename
Frames from the same video, neighboring photos from one inspection route, patches from one wall, and near-duplicate augmentations are related observations. Group them together so that a group belongs to only one partition. Use site, road, structure, capture session, or parent-image identifiers as grouping keys.
OpenCrack describes deduplication across splits and stratification by source, crack presence, and width band. Apply the same principle to your own data: remove exact and near duplicates, then check that each partition contains the relevant sources and crack-width ranges.
Keep a fixed held-out test set
The proportions are a design choice, not a law. The Indian Footpath paper describes a 70:15:15 training, validation, and test partition. The Ultralytics documentation lists 3,717, 200, and 112 images for its three partitions. Use a similar proportion only when it suits your number of independent scenes; a smaller but genuinely independent test set is more informative than thousands of leaked patches.
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Test the deployment domain separately when needed
OpenCrack distinguishes close-up in-domain imagery from wider-geometry road-inspection imagery. If your deployment camera, distance, surface, or route differs from training data, reserve an out-of-domain evaluation group. Report its result separately instead of combining it with easy, closely matched images.
6. Preprocess without erasing narrow cracks
Preprocessing should make the data fit the model while preserving the signal the model must learn. There is no universally correct resize or enhancement recipe.
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Select input size from the smallest relevant crack
Downscaling can make a hairline crack disappear or reduce it to a few unstable pixels. Compare the smallest cracks in your source images with the proposed model input size and inspect overlays after resizing. The documented Ultralytics example trains at an image size of 640, but that example is not evidence that 640 is correct for every camera or crack width.
Use the right interpolation for masks
Resize or warp the image with an image-appropriate interpolation, but treat a categorical mask differently. Do not blend class IDs as if they were colors: interpolating 0 and 1 can create invalid intermediate labels, and interpolating 0 and 255 can create gray values that your loader may misinterpret. Use a nearest-neighbor style operation for categorical masks, then verify that only permitted class values remain.
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For every flip, rotation, crop, perspective change, or scale applied to an image, apply the identical transform to its mask using mask-safe interpolation. A transformed image paired with the original mask is a mislabeled example, even if the filenames still match.
Keep photometric changes image-only and plausible
Brightness, contrast, color, or noise adjustments alter pixels but not geometry, so they belong on the image and not on the mask. Use them only when they resemble deployment variation. Aggressive enhancement can amplify surface texture and create false crack-like patterns; evaluate the transformed overlays and held-out results before adopting it.
Audit patches at tile edges
When tiling, inspect patches containing cracks at every edge and corner. Overlap tiles when necessary, retain the parent identity, and ensure that reconstruction or inference logic can merge predictions without creating duplicated or missing segments.
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Quality control is cheaper before a model has learned from an error. Build checks into ingestion and review a visual sample from every source and annotation batch.
Automated file checks
- Confirm that each image has one intended mask and that no mask is orphaned.
- Compare dimensions and orientation metadata.
- Verify that mask values are exactly the documented class values.
- Detect unreadable files, zero-byte files, unexpected color channels, and accidental palette conversions.
- Check that crop coordinates and parent identifiers exist for every patch.
- Record the number of positive pixels and flag implausibly empty or almost-all-positive masks for human review.
Overlay review
Render the mask over the image with transparency and inspect alignment, thin-crack continuity, branches, intersections, shadows, joints, and textured regions. Review both positive and negative images; a background-only image is valuable for detecting false-positive texture labels.
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Pilot and adjudication
Before labeling thousands of images, have at least two people label a small representative pilot or have one person label and another adjudicate it. Update the policy when disagreements reveal an unhandled case, then recheck earlier labels affected by that rule. Keep policy versions with the annotation batch so a later model run can be traced to the rule used.
8. Build a training manifest
A manifest makes the dataset reproducible and prevents hidden assumptions in scripts. At minimum, store:
- image path and mask or polygon path;
- semantic or instance task type;
- class names and numeric values;
- original and current dimensions, orientation, and crop coordinates;
- parent scene, route, site, or capture-session identifier;
- split assignment and the rule used to assign the group;
- surface, viewpoint, lighting, and environmental descriptors;
- annotation policy version and review status;
- source dataset and license information.
Keep the source license with the record. OpenCrack states that its benchmark documentation is CC-BY-4.0 but does not relicense the underlying images or annotations; each source dataset retains its own terms. Verify the original provider’s conditions before redistribution or commercial use, because availability and licenses can change.
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Evaluate on the held-out images and preserve the split when comparing preprocessing or model versions. The Scientific Data paper reports Dice, IoU, accuracy, precision, recall, and F1 for its segmentation task. Choose metrics that reflect your operational cost: missing a narrow crack and producing a false positive on rough texture may not have the same consequence.
Compare metrics only when the task, mask definition, resolution, and evaluation subset are compatible. OpenCrack cautions that annotation granularity differs across sources and that segmentation metrics should use the pixel-mask subset. Do not mix polygon-instance scores with binary-pixel scores or treat masks drawn under different boundary rules as equivalent ground truth.
Break down errors by surface, crack-width band, lighting, viewpoint, and obstruction. A single overall score can hide the failure mode that matters most in deployment. Keep the test images untouched while investigating; use a separate development subset for policy and preprocessing changes.
10. Troubleshoot common preparation failures
| Symptom | Likely cause | Correction |
|---|---|---|
| Loss falls, but overlays show predictions beside the cracks | Image and mask are shifted, rotated, or paired incorrectly | Recheck dimensions, orientation, crop coordinates, naming, and transparent overlays before retraining |
| Mask files contain unexpected gray values | Categorical masks were resized or augmented with ordinary image interpolation | Use nearest-neighbor-style mask transforms and validate the allowed value set |
| Hairline cracks vanish after preprocessing | Input resize or tile size removes too much spatial detail | Inspect the smallest cracks at target resolution; increase resolution or use carefully designed overlapping patches |
| Validation score is unusually high, then drops in deployment | Near-duplicates, neighboring frames, or patches from one scene crossed the split | Group by parent scene, route, site, or session; deduplicate and rebuild the partitions |
| Model marks rough concrete and shadows as cracks | Negative examples lack the deployment texture or lighting, or the annotation policy treats these cases inconsistently | Add representative negatives, clarify the boundary rule, and review overlays by condition |
| Training fails or predicts no positives | Loader expects different class values or mask format | Compare the manifest, loader configuration, and actual pixel values; test one image–mask pair end to end |
| Performance differs sharply between datasets | Source imagery, annotation granularity, or deployment geometry differs | Compare like-for-like subsets and report in-domain and out-of-domain results separately |
11. A repeatable preparation sequence
- Write the deployment question and choose semantic or instance segmentation.
- Define classes, mask values, file formats, boundary rules, and difficult-case examples.
- Collect images covering deployment surfaces, widths, lighting, viewpoints, texture, and obstructions.
- Create one-to-one names and a manifest that preserves source and parent-scene identity.
- Annotate a pilot, review overlays, adjudicate disagreements, and version the policy.
- Annotate the remaining images, checking every batch for missing labels, invalid values, and alignment.
- Split by independent source groups before tuning; deduplicate and check for leakage.
- Choose input resolution and patching from the narrowest crack you must detect.
- Apply geometric transforms to image and mask together, mask-safe interpolation to labels, and plausible photometric changes to images only.
- Run file checks and overlay review, then train with a fixed validation set.
- Evaluate on a locked test set, using metrics appropriate to pixel segmentation or instance output and reporting the conditions represented.
If these steps are explicit in the dataset record, another engineer can reproduce the labels, understand what a positive pixel means, and determine whether a model failure comes from data coverage, annotation policy, geometry, preprocessing, or genuine generalization limits.
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