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Evaluate crack-segmentation models with a fixed, leakage-controlled test protocol and several complementary measures. Use IoU and Dice for region overlap, a boundary metric such as Boundary F1 or normalized surface Dice (NSD) for contour placement, and a connectivity-aware score such as clIoU for breaks and disconnected traces. Report thin-crack subsets, verified crack-free negatives, per-source results, and separate close-up and wide-scene tests. A single overall IoU can conceal failures on one-pixel cracks, fragmented masks, or deployment images that differ from training photographs.
Why thin and fragmented cracks defeat a single score
Crack masks are unusually sensitive to annotation thickness, pixel alignment and continuity. A prediction can follow the visible path of a hairline crack yet overlap relatively few labeled pixels. Conversely, a mask can obtain respectable area overlap while containing gaps that make it unusable for tracing, length estimation or maintenance decisions.
These failure modes are different, so the evaluation must be multi-dimensional:
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- Boundary alignment: whether the predicted contour lies where the crack edge is annotated.
- Connectivity: whether a predicted trace remains continuous instead of breaking into isolated pieces.
- False alarms: whether seams, joints, texture and other crack-like structures are incorrectly labeled.
- Generalization: whether performance survives changes in material, camera, scale and acquisition setting.
For a fair comparison, every model must use the same held-out images, preprocessing policy, post-processing policy and metric implementation. The test cohort must remain untouched during model selection.
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Build a test set that can expose the real failure modes
Deduplicate before splitting
Near-identical frames, crops and augmentations can leak visual information between training and test sets. Deduplicate across all splits before evaluation. OpenCrack describes a consolidation with cross-split deduplication and a stratified 70/15/15 split. Its stratification covers source, crack presence and crack-width band. Those are project-reported properties, not independent audits, but they illustrate the controls a benchmark should document.
Hold out sources, not just random images
A random image split tests interpolation within the same collection. A source-held-out split tests whether the model transfers to a different camera, surface, lighting setup or annotation style. When the data permit it, reserve entire source datasets or acquisition campaigns for testing and publish the source identity for each result.
Include positives and verified negatives
A segmentation benchmark made only of crack-positive images cannot measure how often a model hallucinates cracks. Add verified crack-free images, plus hard negatives containing seams, joints, stains, aggregate texture, shadows and construction markings. OpenCrack reports a confirmed-negative pool of 11,229 images for false-positive evaluation.
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Stratify the held-out cohort
At minimum, identify each test image by source and crack presence. Add crack-width bands when the annotation supports a reliable width measurement. Report the number of images in every reported subset so a very small thin-crack group cannot dominate interpretation.
Freeze the test cohort
State whether any test image, label or aggregate score influenced architecture choice, threshold selection, augmentation design or early stopping. If it did, the cohort is no longer an unbiased final test; create a new untouched evaluation set.
Use complementary metrics, not a winner-takes-all leaderboard
IoU and Dice for region overlap
Intersection over Union (IoU) summarizes the intersection of predicted and labeled crack regions relative to their combined area. Dice, also called the F1 score for masks, weights the same overlap relationship differently. Report both because they respond differently to the same amount of disagreement, especially when the crack occupies few pixels.
IoU should never be the only headline number for hairline cracks. The OpenCrack nnU-Net model card warns: “IoU is unfair to one-pixel-wide cracks (a perfect prediction can score ≈0.48).” Treat that as the model card’s explanation of IoU behavior for a one-pixel-wide mask, not as a universal performance result. The practical implication is to publish overlap together with boundary and connectivity measures.
Boundary F1 or normalized surface Dice
Boundary F1 evaluates whether predicted and reference contours agree within a stated tolerance. NSD likewise evaluates surface agreement under a defined distance tolerance. Either can reveal a mask that is slightly displaced around a thin line even when its area is small.
Always disclose the tolerance, pixel spacing or image-resolution convention, boundary extraction method and implementation version. The metric names alone are not enough to reproduce a score.
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Connectivity-aware scoring
Use clIoU or another explicitly stated skeleton/connectivity measure to expose breaks and disconnected predictions. Include the tolerance parameter in the name and table; for example, clIoU(τ=4) means the reported score used a tolerance of four in that implementation. Do not assume that a clIoU value with a different tolerance is comparable.
For genuinely fragmented masks, define what constitutes a missed gap. A one-pixel interruption may be irrelevant for one downstream task and a critical failure for another. Base the rule on annotation policy and the intended use. If you apply a gap-closing or skeleton-joining operation, apply exactly the same operation to every model and disclose it.
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False-positive rate or specificity
Evaluate crack-free images separately and report a false-positive rate or specificity, along with recall on crack-positive images. Include a hard-negative breakdown so readers can see whether errors come from expansion joints, pavement texture, corrosion patterns or shadows.
Make thin-crack performance visible
Predeclare a width definition
“Thin” is not a universal category. State whether width is measured from the labeled mask, a distance transform, a skeleton-to-boundary measurement or another method. Specify whether the value is in pixels or physical units and how variable-width cracks are assigned to a band.
FACS-Net reports a thin-crack subset at width ≤2 pixels on CrackVision12K. Use that threshold as a study-specific example, not as a standard cutoff for every camera or resolution.
Publish width-stratified scores
At least one table should contain overlap, boundary and connectivity scores for each predeclared width band. Keep the all-width score, but do not let it substitute for the thin subset. A wide crack can contribute many correctly classified pixels and hide a complete failure on hairline traces.
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Pixel width changes when an image is resized. Record the original resolution, resize operation and the resolution used for evaluation. A ≤2-pixel group at one acquisition scale is not equivalent to a ≤2-pixel group at another.
Inspect examples, not only averages
Show representative true positives, missed thin traces, overgrown masks and broken predictions. Pair each image with the reference mask and the same color scale for every model. Qualitative panels do not replace metrics, but they explain why a score changes.
Separate close-up and wide-scene generalization
Close-up surface photographs and wide road-inspection scenes are different operating regimes. They differ in crack-to-image area, perspective, blur, illumination, background texture and the amount of irrelevant surface. Combine them into one leaderboard only if the intended deployment genuinely mixes both regimes and the weighting is explicitly justified.
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Report in-domain results
In-domain evaluation uses the same acquisition regime represented during training. Give the source, split, number of images and every metric aggregation.
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Report held-out-domain results
Use a separate cohort for a different camera, material, scale or dataset. OpenCrack contrasts its close-up OpenCrack regime with PaveSafe wide road scenes. In the project comparison, nnU-Net records IoU 0.539 in-domain and 0.142 on PaveSafe. The project reports all crack-trained models in a 0.09–0.14 IoU band on the wide-scene cohort and says model ranking depends on acquisition regime.
Those figures are benchmark-specific, not a universal conversion between close-up and road-scene accuracy. They demonstrate why a close-up leaderboard cannot answer whether a model is ready for road inspection.
Break results down by source
Per-source results expose a model that succeeds on one material and fails on another. Include source-level sample counts and confidence intervals where feasible. A single macro average can be dominated by a small source if counts are not shown, while a micro average can be dominated by the largest source.
Choose and report aggregation correctly
Image-weighted (micro) summaries
A micro summary weights images according to how many are present in the cohort. It answers, “What score would a randomly selected test image receive?” This is useful for estimating aggregate workload but can conceal poor performance on small sources.
Source-weighted (macro) summaries
A macro summary gives each source equal influence after calculating source-level scores. It answers, “How consistently does the model perform across sources?” Publish the source-level values as well; the macro number without its components is difficult to audit.
Uncertainty intervals
Provide confidence intervals and the test-cohort size. OpenCrack reports 1,000-resample bootstrap confidence intervals for its comparison on 6,910 positive images. Match the resampling unit to the data structure: if many images come from one inspection sequence, resampling individual frames can understate uncertainty. State the unit and procedure.
Do not compare unlike aggregates
Two IoU values are not directly comparable when one is micro and the other is source-macro, or when one includes negatives and the other reports positive images only. Put the aggregation type, cohort definition and metric implementation beside every number.
What benchmark results can—and cannot—tell you
| Benchmark or comparison | Reported scope | Values and qualification |
|---|---|---|
| OpenCrack nnU-Net comparison | OpenCrack held-out positive cohort; 6,910 positive images; 1,000-resample bootstrap intervals | IoU 0.539; Dice 0.664; Boundary F1 0.608; NSD 0.700; clIoU(τ=4) 0.641 |
| OpenCrack versus PaveSafe for nnU-Net | Close-up in-domain regime versus wider road-scene cohort | IoU 0.539 in-domain; IoU 0.142 on PaveSafe |
| All crack-trained models on PaveSafe | Project comparison on the wide-scene cohort | Reported IoU band 0.09–0.14; project documentation says model ranking depends on acquisition regime |
| OmniCrack30k nnU-Net evaluation | 30,000 samples from more than 20 datasets covering asphalt, ceramic, concrete, masonry and steel | Mean clIoU_4px of 64% under that paper’s benchmark and protocol |
| FACS-Net thin-crack result | ≤2-pixel subset on CrackVision12K | Abstract claims gains over Hybrid-Segmentor; the result is paper-specific and was not independently reproduced here |
Do not place the OmniCrack30k clIoU_4px value beside the OpenCrack clIoU(τ=4) value and call the larger one the winner. The datasets, splits, aggregation and implementation may differ. Match all of those conditions before making a cross-benchmark comparison.
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OpenCrack’s documentation says, “The ranking is identical across all five per-image metrics, so no conclusion depends on the metric choice.” That statement applies to the evaluated test cohort and models; it does not establish that metric choice is irrelevant for every dataset or architecture.
A reproducible evaluation procedure
- Define the deployment question. Decide whether the model must trace close-up material, detect cracks in wide road scenes, preserve continuity for measurement, or minimize false alarms in inspection workflows.
- Lock the data version. Record dataset releases, source identities, annotation versions, image resolution and any exclusions.
- Remove leakage. Deduplicate near-identical images and related frames across training, validation and test partitions.
- Create the split. Prefer source-held-out evaluation for generalization; otherwise document the randomization and stratification variables.
- Define width bands. State the measurement method, pixel or physical units, resize policy and thin-crack threshold before running models.
- Freeze preprocessing. Apply the same resizing, normalization, tiling, threshold selection and post-processing policy to every model, or report model-specific policies explicitly.
- Run every model on the identical cohort. Keep predictions, probability maps, thresholds and software versions for audit.
- Calculate complementary measures. Report IoU and Dice, Boundary F1 or NSD, a connectivity score such as clIoU with its tolerance, and false-positive measures on negatives.
- Stratify the output. Publish all-width, width-band, source, crack-presence and acquisition-regime results when sample counts support them.
- Quantify uncertainty. Give cohort sizes, confidence-interval method, resampling unit and micro/macro aggregation.
- Inspect failure cases. Review thin misses, broken traces, over-segmentation and hard-negative false alarms; connect each pattern to the metric that reveals it.
- Preserve the final test. Use a new untouched cohort for any decision made after seeing the reported results.
Common evaluation mistakes and fixes
Using IoU as the sole score
Symptom: a model with visually continuous traces ranks below a model with chunky masks. Fix: add a boundary metric and connectivity-aware metric, then inspect thin-width strata.
Declaring a universal thin-crack threshold
Symptom: every dataset labels “thin” as ≤2 pixels regardless of camera scale. Fix: predeclare a width method and justify the threshold for that acquisition; identify the FACS-Net ≤2-pixel split as a study-specific choice.
Closing gaps only for one model
Symptom: a post-processing script joins fragments for the preferred model but not its competitors. Fix: apply the identical operation to all predictions or report raw and processed results separately.
Testing only crack-positive images
Symptom: recall looks high while field deployment produces many alerts. Fix: add verified negatives and hard negatives, then report false-positive rate or specificity.
Randomly mixing acquisition regimes
Symptom: a high average score is presented as evidence for road inspection even though training and testing use close-up photos. Fix: publish in-domain and held-out-domain results separately, including wide-scene cohorts.
Hiding the aggregation rule
Symptom: two papers appear to disagree by several points. Fix: label every score as micro or macro, provide per-source values and state whether negatives are included.
Overstating a paper-specific result
Symptom: an abstract’s claimed gain is treated as an independently verified fact. Fix: identify the paper, dataset, subset and comparator, and say when the result has not been reproduced under your protocol.
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Choose a model against the failure cost of your application, not a single universal ranking.
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- For continuous tracing or crack-length measurement: prioritize connectivity scores and inspect gap failures, while retaining boundary and overlap measures.
- For hairline detection: require a width-stratified result and boundary metric; an all-width IoU can hide the relevant cases.
- For automated alerts: set an acceptable false-positive rate using verified negatives and hard negatives, then examine recall at that operating point.
- For mixed deployment environments: require source-held-out or acquisition-held-out evidence and report each regime separately.
- For a benchmark leaderboard: publish micro and source-macro summaries, per-source scores, uncertainty intervals, cohort definitions and metric tolerances.
A model that wins on overlap but breaks traces may be unsuitable for measurement. A model with strong continuity but frequent seam detections may be unsuitable for unattended alerts. The evaluation should make that trade-off visible rather than compressing it into one rank.
Minimum reporting checklist
- Dataset and version, source labels, material types and image counts.
- Deduplication method and split policy, including whether sources were held out.
- Positive and verified-negative cohort sizes, with hard-negative categories.
- Crack-width definition, measurement units, resize policy and thin threshold.
- IoU and Dice values with micro or macro aggregation identified.
- Boundary F1 or NSD with tolerance and implementation details.
- Connectivity metric, tolerance and connectivity convention.
- False-positive rate or specificity, plus positive recall.
- Per-source, per-width and per-acquisition-regime results.
- Confidence-interval method, resampling unit and number of resamples.
- Thresholding, tiling, augmentation and post-processing applied at evaluation.
- Whether the test cohort influenced model selection.
- Representative failure cases and a record of software and metric versions.
Limits of published scores
Metric names do not guarantee identical implementations. Boundary tolerance, pixel connectivity, clIoU definitions, NSD computation and treatment of empty masks can vary. The benchmark harness should be inspected before reproducing or comparing exact values. Underlying source datasets also retain their own licenses; OpenCrack’s documentation license does not relicense the images or annotations it consolidates.
No universal crack-width cutoff or universal benchmark was established. Treat every threshold, score and ranking as conditional on its images, labels, acquisition regime, aggregation and implementation.
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The defensible evaluation is a matrix, not a single number: IoU and Dice for area overlap; Boundary F1 or NSD for contour placement; clIoU or another disclosed connectivity measure for continuity; width-stratified results for thin cracks; verified negatives for false alarms; and source- or acquisition-held-out tests for generalization. Freeze the cohort, disclose every tolerance and aggregation rule, and publish the failures that a deployment decision will actually encounter.
Frequently Asked Questions
Which metric is best for thin crack segmentation?
None is sufficient alone. Pair IoU and Dice with Boundary F1 or NSD and a connectivity-aware score such as clIoU, then report a predeclared thin-width subset.
Why can IoU look low when a crack is only one pixel wide?
A small contour displacement changes a large share of the labeled pixels in a hairline mask. The OpenCrack nnU-Net model card explains that a perfect one-pixel-wide mask can be represented by an IoU of approximately 0.48 under its example, so boundary and connectivity measures are needed for context.
How can I test whether a predicted crack mask is connected?
Use clIoU or another explicitly defined skeleton/connectivity metric, disclose its tolerance and connectivity convention, and inspect gap cases against the annotation policy. Apply any gap-closing post-processing identically to every model.
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