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Reliable defect segmentation starts before a model sees an image. Choose the inspection modality, preserve acquisition context, define exactly what a mask means, and split data by module so related cells do not leak between training and evaluation. This guide focuses on electroluminescence (EL) images of crystalline-silicon cells and modules, then explains why outdoor infrared (IR) thermography requires a separate capture and labeling policy.
The workflow below covers acquisition records, geometric correction, cropping, mask design, review, dataset structure, leakage control, metrics, and practical failure recovery. Reported dataset sizes and model scores are identified with their original scope; they are not universal performance promises.
1. Define the inspection task before collecting images
Write a one-page task specification before opening an annotation tool. It should name the modality, the physical subject, the target defects, and the required output. A model trained on complete-module EL images has different preprocessing and labels from one trained on isolated bare cells; an outdoor thermal image cannot be interpreted with EL instructions.
Choose EL, outdoor IR, or another modality
EL imaging makes electrically inactive areas, cracks, and related cell defects visible when a crystalline-silicon cell is driven under forward bias. IEC TS 63202-2:2021 specifies methods for detecting and examining defects in bare crystalline-silicon cells with EL, including capture guidance and defect information. The standard was published on 16 December 2021; check the IEC catalogue for the current lifecycle and edition before using it as a compliance reference.
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Outdoor infrared thermography is a different inspection method. IEC TS 62446-3:2017 addresses thermographic inspection of PV modules and plants, including equipment, ambient conditions, procedures, reporting, personnel qualifications, and a matrix of thermal abnormalities. IR temperature patterns are affected by operating state, irradiance, wind, reflections, and emissivity. Do not merge EL and IR samples under one label guide unless every record carries modality metadata and the instructions explicitly define modality-specific appearances.
Choose the output granularity
- Classification: one label or probability for a cell, module, or image.
- Detection: locations represented by boxes, points, or polygons.
- Semantic segmentation: a class assignment for each pixel, normally including background.
- Instance segmentation: separate masks for individual cracks, hotspots, or other instances.
Decide whether the model must find an entire crack network, each crack segment, a dark inactive region, a busbar, or several overlapping classes. A cell-level defect probability is not a pixel mask. If you derive a mask from an image-level label, record it as a pseudo-label or derived annotation rather than presenting it as hand-drawn ground truth.
2. Preserve acquisition context and provenance
Keep the original file untouched. Every processed image and mask should be traceable to a source module or cell and to the conditions under which it was captured. Provenance is essential for diagnosing a model that appears accurate only because it has seen nearly identical cells during training.
Minimum metadata record
| Field | What to record | Why it matters |
|---|---|---|
| Source identifier | Stable module ID, cell ID, image ID, and parent-module relationship | Enables grouped splits, traceability, and re-labeling |
| Modality | EL, outdoor IR thermography, or another defined modality | Determines capture assumptions and label appearance |
| Date and location | Capture date, site or laboratory, and relevant exposure history | Exposes changes in environment, aging, and equipment |
| Equipment | Camera, lens, illumination or bias hardware, and calibration identifier where known | Helps explain resolution, distortion, noise, and domain shifts |
| Acquisition settings | For EL: forward-bias and exposure settings where available; for IR: operating state, ambient conditions, and radiometric settings where available | Allows comparable subsets and failure analysis |
| Original geometry | Raw width, height, orientation, bit depth, and file format | Prevents silent changes during conversion |
| Processing history | Calibration file, crop coordinates, perspective transform, resize method, normalization, and software version | Makes preprocessing reproducible |
| Label status | Annotator, reviewer, version, disagreement status, and uncertainty flags | Separates draft, adjudicated, and excluded masks |
Sandia National Laboratories’ PV-IV-EL catalogue illustrates the value of paired context: it links EL images with IV traces, acquisition metadata, and outdoor-exposure information. The catalogue describes 613 corresponding IV/EL measurement sets covering 438 unique modules across 28 models and 17 brands. Those figures describe that collection, not a required sample size.
Keep module and cell identities intact
Use immutable identifiers rather than filenames that change when an image is cropped. A practical manifest can include columns such as module_id, cell_id, image_path, mask_path, modality, capture_date, source_split, label_version, and quality_status. If a complete module is tiled into cells, store the tile coordinates and the parent image ID. Never discard the parent relationship after extraction.
3. Prepare images without destroying evidence
Apply only transformations required by the task, and retain an untouched source plus machine-readable transform parameters. A processed image should be reproducible from its source, calibration data, and a documented pipeline version.
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Correct lens distortion when calibration supports it
Wide-angle or close-range optics can bend straight cell edges. Use a documented camera calibration to undistort images when the distortion would affect cell boundaries or crack geometry. Save the calibration identifier and interpolation method. Do not undistort an image with an unknown or inappropriate calibration merely because the result looks straighter; interpolation can alter thin cracks and create edge artifacts.
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Perspective normalization is useful when modules are photographed obliquely and the model should receive consistently aligned cells. Detect or manually define module and cell boundaries, then record the transform matrix and crop coordinates. Keep a visual quality check for clipped borders, missing corners, and duplicated cells.
The Jülich EL dataset provides a concrete, dataset-specific example: images were normalized by size and perspective, lens-induced distortion was removed, and the result was stored as 300 × 300 pixel, 8-bit grayscale cell crops. It contains 2,624 cell images from 44 PV modules. These dimensions and bit depth are not universal input requirements; they describe that release.
Crop and resize consistently
When starting with a complete-module EL image, crop individual cells only after confirming the grid geometry. Chen and colleagues reported an automatic cell-cropping stage that took approximately 0.72 seconds per module in their implementation. That timing depends on their code and hardware and should not be used as a general throughput guarantee.
Choose one interpolation policy and apply it consistently. Area or bilinear resizing may be suitable for ordinary intensity images, but mask resizing must use nearest-neighbor interpolation so class IDs are not blended. If a crack is only a few source pixels wide, measure its apparent width before and after resizing; a target resolution that erases the structure cannot be repaired by annotation.
Handle intensity and bit depth explicitly
Record whether grayscale values are linear, gamma-adjusted, radiometric, or display-rendered. Keep the original bit depth, then define normalization from measured ranges rather than from a screenshot. If you convert to 8-bit for a model, preserve the conversion parameters and keep the higher-bit source for audit and reprocessing. Avoid per-image normalization when absolute intensity carries diagnostic meaning unless the task specification explicitly permits it.
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Separate quality control from enhancement
Flag saturated, blurred, motion-affected, severely occluded, or incorrectly exposed images. Do not silently sharpen, denoise, inpaint, or erase borders in a way that changes defect evidence. If enhancement is necessary, store the enhanced derivative beside the raw and label it in metadata. A model should not learn that a particular enhancement artifact is a defect class.
4. Design the annotation schema before production labeling
Write the labeling guide before assigning a large batch. Include examples of normal cells, each defect class, borderline cases, and explicit exclusions. The guide should be specific enough that two trained annotators can make the same decision without asking the project owner for an informal ruling.
Set the spatial level and class ontology
- Cell or module status: use when the operational decision is simply defective or acceptable.
- Defect-region mask: use when the model must delineate dark regions, inactive areas, hotspots, or other contiguous regions.
- Crack mask: define whether every visible branch is included and whether sub-pixel or uncertain traces are excluded.
- Busbar or grid mask: include only when it is a target class or needed as a structural reference.
- Artifact class: add a separate label for glare, dust, stitching seams, compression marks, or other non-defect structures if they could be mistaken for defects.
Decide whether classes can overlap. A crack crossing a dark inactive region may require multilabel masks, a priority order, or an instance representation. Document the choice; changing it halfway through a dataset makes class counts and metrics incomparable.
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Define boundaries and ambiguous pixels
State whether the mask follows the visible intensity transition, the estimated physical defect extent, or a conservative inner boundary. Explain how to treat busbars, cell borders, ribbons, and areas outside the active cell. For a faint crack that cannot be resolved, use an explicit uncertain or ignore region rather than forcing a positive or negative decision. Store an ignore mask if the training framework supports it.
Use a stable mask representation
Store a machine-readable mask linked to the image ID. A single-channel indexed PNG can represent mutually exclusive classes; separate binary channels or a run-length format may be better for overlapping labels. Define the class-to-value mapping in the manifest, protect masks from lossy compression, and validate that image and mask dimensions match exactly.
Distinguish human labels from derived labels
The Jülich release provides defect probability and cell type for each cell image, while crack-segmentation work uses pixel masks for semantic segmentation. These annotations answer different questions. Keep image-level probabilities, polygons, pixel masks, and pseudo-labels in separate fields and never silently substitute one for another.
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5. Run a controlled annotation workflow
- Assemble a pilot set. Sample normal cells, every intended defect class, difficult low-contrast examples, different cameras or sites, and complete-module edge cases.
- Draft the guide. Add positive and negative examples, boundary rules, uncertain-region handling, class-overlap policy, and exclusion criteria.
- Double-label the pilot. Have at least two annotators label the same images independently. Record disagreements by class and by cause, not only as a single agreement score.
- Adjudicate and freeze version 1. A reviewer resolves disagreements, updates the guide, and assigns a schema version to every accepted mask.
- Label production batches. Keep batch IDs, annotator IDs, timestamps, and tool versions. Do not overwrite an earlier mask when a correction is made.
- Review systematically. Inspect all rare classes and a random sample of common classes. Check alignment, boundary consistency, missing masks, and accidental class-value changes.
- Adjudicate difficult cases. Preserve both the original and reviewed mask, the reason for the decision, and any ignore region.
- Lock the evaluation set. Once labels are reviewed, restrict access and record any later changes as a new evaluation version.
A 2022 preprint describing a scalable annotation framework reported a 60% reduction in annotation cost on its evaluated validation task. Treat that as a study-specific result: it is not a guaranteed saving for another defect taxonomy, tool, workforce, or image quality.
6. Split data to prevent module leakage
Cells cut from one module share manufacturing patterns, illumination, camera geometry, and often the same defect event. Randomly assigning those cells to training and test sets can produce an overly optimistic score because the model has effectively seen the source module already.
Group by the highest available source unit
Where feasible, assign every crop from a module to a single split. If modules come from several sites, manufacturers, models, or cameras, record those groups so you can create an evaluation subset that tests the intended deployment shift. The recommendation to split by module is a methodological safeguard inferred from the grouped structure of module datasets, not a stated requirement of the cited catalogues.
Choose train, validation, and test roles
- Training: images used to fit model parameters and permitted augmentation choices.
- Validation: grouped images used for threshold, architecture, and early-stopping decisions.
- Test: a locked grouped set used once for the headline result.
- External or stress test: a different site, camera, exposure condition, or module population when available.
Publish the number of modules and cells in each split, class prevalence, excluded images, and the grouping key. A cell count alone is not enough to judge independence.
7. Review quality and report segmentation metrics
Quality checks before training
- Verify one-to-one image/mask links and identical pixel dimensions.
- Check that every class value is in the approved ontology.
- Render random overlays at native resolution to catch shifts and flipped axes.
- Measure the number of positive pixels per class and inspect extreme outliers.
- Compare label density by annotator, module, camera, and split for systematic differences.
- Confirm that rejected or uncertain images are not silently counted as normal negatives.
Metrics to report
| Metric | What it conveys | Required qualification |
|---|---|---|
| Intersection over union (IoU) | Overlap between predicted and reference pixels divided by their union | Report per class and state whether averages are macro, weighted, or micro |
| Precision | How many predicted positive pixels are correct | Important when false alarms are costly |
| Recall | How many reference positive pixels are found | Important when missed defects are costly |
| F1 score | Harmonic mean of precision and recall | State thresholding, averaging method, and evaluation split |
| Boundary or distance measure | How closely predicted edges follow the reference boundary | Useful for thin cracks, where area overlap can hide edge errors |
Always state the mask policy, ignored pixels, class balance, preprocessing, split unit, and evaluation-set construction beside the score. Chen and colleagues reported an average test F1 of 0.875 and IoU of 0.782 for their crack-segmentation method, which used a VGG16-encoder U-Net and its own test set. Those values are not expected results for a different camera, taxonomy, resolution, or split design. Their implementation also reported approximately 0.18 seconds per cell for crack segmentation; that is an implementation-specific measurement.
8. Useful reference collections and what each can support
| Collection or publication | Documented characteristics | Appropriate use | Important limitation |
|---|---|---|---|
| Jülich EL Dataset, version 1.5 | 2,624 grayscale cell images, 300 × 300 pixels, 8-bit, from 44 modules; size and perspective normalization and lens-distortion correction are described | Studying cell-level EL classification or adapting a documented crop pipeline | Defect probability and cell type are not equivalent to pixel-level crack masks |
| Sandia PV-IV-EL catalogue | 613 paired IV/EL measurement sets, 438 unique modules, 28 models, 17 brands, with acquisition and exposure context | Linking EL appearance with electrical measurements and provenance-aware evaluation | Check the record for the exact mask availability and label granularity before treating it as a segmentation set |
| Chen et al., IEEE Journal of Photovoltaics, 2023 | Automatic cell cropping, crack and busbar segmentation, VGG16-encoder U-Net, public annotated benchmark, and reported runtime and test metrics | Comparing a published crack-segmentation setup when your split and labels are compatible | Scores and timings are tied to that implementation and test set |
| Scalable annotation framework preprint, 2022 | Reports a 60% annotation-cost reduction on an evaluated validation task | Considering assisted annotation and review economics | Preprint result; verify method, publication status, and applicability to your task |
| NREL visual inspection collection tool, Packard and Kurtz, 2012 | Fourteen documentation sections; evaluated on more than 60 modules from more than 20 manufacturers at two sites | Designing field-condition metadata and inspection records | It is a visual-inspection documentation tool, not a pixel-mask standard |
9. Common failure modes and recovery steps
EL and IR images were combined without modality labels
Separate the records by modality, revise the manifest, and decide whether to train separate models or use an explicit modality input. Revisit every label whose appearance depends on thermal behavior rather than EL intensity.
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Thin cracks vanished after resizing
Compare source and target pixel widths, increase the target resolution or crop scale, and regenerate masks from the source coordinates. Do not attempt to restore information with sharpening alone.
Predictions look excellent on random cells but fail on new modules
Rebuild the split by module, retrain without leaked cells, and report the grouped result. Add a module-level or site-level stress test if deployment involves a new camera or population.
Annotators disagree about faint lines and borders
Add explicit examples and an ignore-region rule, then relabel a pilot batch under the revised guide. Track disagreements by defect type; a single overall agreement number can conceal a failure concentrated in the class that matters most.
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Masks are shifted by one or more pixels
Check crop coordinates, image orientation, EXIF handling, calibration interpolation, and resize order. Render overlays before and after every geometric operation and regenerate masks from the same transform parameters as the images.
Class metrics are unstable
Report per-class pixel counts and module counts, inspect rare-class prevalence by split, and avoid relying on a single aggregate score. Consider confidence intervals or repeated grouped splits when the number of independent modules is small.
10. Release checklist
- Modality and physical subject are stated, with EL and outdoor IR policies kept distinct.
- Raw images, acquisition metadata, module IDs, and exposure or operating context are retained.
- Every transformation has documented parameters and a pipeline version.
- Image-level labels, polygons, pixel masks, and pseudo-labels are explicitly differentiated.
- Defect classes, overlap rules, boundaries, uncertain regions, and exclusions are frozen in a versioned guide.
- Image and mask files use stable IDs, lossless mask storage, and validated dimensions.
- Pilot double-labeling, adjudication, reviewer checks, and mask history are recorded.
- Train, validation, and test sets are grouped by module where feasible, with counts and class balance reported.
- Metrics include per-class IoU and precision/recall or F1, plus the exact evaluation construction.
- Any published speed, cost, or accuracy figure is attributed to its original implementation and conditions.
Following this sequence produces a dataset that can be audited from a model prediction back to a physical module and capture condition. That traceability is what makes a segmentation result useful beyond a single benchmark.
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