The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →For pixel-level defect segmentation, first specify the imaging modality, then preserve the original images and acquisition context, apply documented geometric corrections, and define a mask policy before production labeling. Electroluminescence (EL) is the best-supported modality in the examples discussed here: it reveals defects in crystalline-silicon cells under forward bias. Outdoor infrared thermography (IR) is a different inspection method, with different capture conditions and interpretation rules. A mask policy or preprocessing pipeline built for one should not silently be applied to the other.
What does a usable solar-image segmentation dataset need?
A segmentation dataset pairs each source image with a mask that identifies the pixels belonging to one or more target classes. The image and its mask must share the same geometry: if an image is cropped, resized, or perspective-corrected, the corresponding mask must undergo the identical spatial transformation. Pixel labels are not interchangeable with cell-level labels, defect probabilities, or image-level classifications. Those labels can support other tasks, but they do not specify which pixels a segmentation model should learn to identify.
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Before collecting or labeling data, write down the intended output and scope. A technically useful specification identifies the modality (EL, outdoor IR, or another image type), whether inputs show individual cells or whole modules, the defect classes to segment, the output granularity, and the inspection conditions represented. Record whether the aim is semantic segmentation, instance-level separation of individual defects, or another task; a class mask alone does not necessarily distinguish one crack from another.
For EL, IEC TS 63202-2:2021 addresses electroluminescence imaging of bare crystalline-silicon cells under forward bias. It describes methods for detecting and examining defects and provides capture guidance and defect information. Its stated scope is bare cells, so do not assume it alone defines every acquisition detail for encapsulated, complete-module imagery. The IEC page gives a publication date of December 16, 2021; check the standard’s lifecycle and current availability before treating that edition as current.
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Outdoor IR inspection has separate guidance: IEC TS 62446-3:2017 covers outdoor thermographic inspection of PV modules and plants, including equipment, ambient conditions, procedure, reporting, personnel qualifications, and thermal-abnormality guidance. The visible patterns and their interpretation depend on the modality and capture conditions. Keep modality in the dataset record and use modality-specific labeling instructions. IEC also notes that other methods may be combined when EL alone does not settle a defect interpretation.
How to plan a reproducible workflow
A stable workflow treats acquisition, preprocessing, annotation, and evaluation as linked parts of one dataset. A mask cannot compensate for missing capture context, and a clear label guide cannot correct an undocumented warp that misaligns images and labels. The following sequence separates those decisions so a team can reproduce them.
- Specify scope. Record modality, bare-cell versus module-level input, target defects, output type, and the conditions under which images were captured.
- Preserve provenance. Retain originals and record module or cell identity, date, equipment and settings where known, site or lab context, and relevant operating or exposure information.
- Prepare images with documented transforms. Correct lens distortion only when supported by calibration; normalize perspective when needed; localize cells or modules; and standardize crops or dimensions for the intended model.
- Publish an annotation policy. Define classes, mask boundaries, uncertainty handling, naming, review, and adjudication before full-scale labeling begins.
- Review and evaluate at the right unit. Keep raw and reviewed masks, document the dataset split and its unit, and report per-class metrics with the evaluation-set construction.
How should images and acquisition context be preserved?
Keep an untouched source file for every image used to derive a training crop. Assign stable identifiers so each crop, mask, and record can be traced back to its source module or cell. A minimum useful record should include the image identifier, source module identifier if available, modality, capture date, image level (cell or module), equipment or camera when known, acquisition settings when available, and site or lab context. For field imagery, record relevant exposure or operating context if it is available. Do not fill unknown values with assumptions; mark them as unknown in the dataset record.
Metadata is not administrative decoration. It lets users interpret what the pixels represent, identify related images, and assess whether a model is being evaluated on conditions comparable to its training data. Sandia National Laboratories’ PV-IV-EL catalog links EL imagery with IV traces and acquisition metadata, including outdoor-exposure information. The catalog record reports 613 corresponding IV/EL measurement sets and 438 unique modules across 28 models and 17 brands; these are counts for that catalog, not a guarantee that every image has every field or that the collection represents all PV installations.
Store each processed image’s relationship to its source and transformation history. A practical record can name the original file, derived crop, mask file, transform version, and annotation-guide version. If an image is corrected, resized, or cropped, preserve parameters or code sufficient to recreate that result. Do not overwrite the original with the corrected image: retaining both helps diagnose alignment mistakes and allows a later pipeline to start from the same input.
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How to prepare EL images without losing traceability
Preprocessing should make images usable for the task without silently changing what a label means. Use only transformations justified by the source optics and model requirements, and apply spatial operations identically to image and mask. Keep a record of crop coordinates, scale, perspective transform, distortion correction, and any intensity conversion. A photometric change such as normalization does not alter pixel coordinates, but can alter the visual appearance on which annotators rely; document it and retain the unmodified source.
Correct geometry only when justified
Lens-distortion correction is appropriate when calibration information supports a correction. Perspective normalization can make cells more consistently aligned when the source view is oblique. Neither is a universal prerequisite: an incorrect correction can deform a crack, shift a boundary, or make a mask misalign. Compare the transformed image with its source, inspect edge regions, and verify that image and mask remain registered after every spatial operation.
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Localize cells and create consistent crops
For whole-module images, a pipeline may first locate individual cells and crop them for annotation or model input. Chen and colleagues’ 2023 crack-segmentation paper describes automatic cell cropping and reports approximately 0.72 seconds per module for cropping in its implementation. That is a result tied to their code and setup, not a general runtime expectation. The same paper reports approximately 0.18 seconds per cell for crack segmentation; this too is implementation-specific.
The Jülich EL dataset provides a concrete example of a processed representation: 2,624 cell images, each a 300 × 300-pixel, 8-bit grayscale crop, drawn from 44 PV modules. Its description says images were normalized by size and perspective and lens-induced distortion was removed before cell extraction. Those dimensions describe that dataset, not a universal resolution or required preprocessing recipe. Choose a crop size based on the source imagery, defect scale, and model task, and retain enough resolution to represent the smallest target features.
What should the annotation policy specify?
Write the policy before production labeling and make it concrete enough for two annotators to make comparable decisions. There is no universal mask ontology established by the sources described here. Your taxonomy and boundary rules are project decisions, not a standard that can be assumed across datasets.
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- Granularity: say whether labels are assigned per cell, module, region, or pixel. If the training target is a pixel mask, state that explicitly.
- Classes: name each target class and say whether classes may overlap. Define whether normal background is explicitly labeled or left as an ignore region.
- Boundaries: explain which pixels count as a defect and how to handle thin cracks, dark regions, busbars, cell borders, and image artifacts. State whether masks follow the visible feature edge or a different operational boundary.
- Uncertainty: define how to mark ambiguous, occluded, clipped, or unresolvable regions. An ignore label is preferable to silently treating uncertain pixels as normal when the annotation cannot support a confident decision.
- Identity and file links: specify how image and mask filenames connect to cell, module, and source identifiers. Define the mask encoding and class IDs in a machine-readable legend.
- Review: specify who checks labels, how disagreement is adjudicated, how corrections are recorded, and which guide version applies to each batch.
Include example images for difficult cases in the guide. Show a typical positive, a hard negative, a boundary case, and an uncertain case if those occur in the project. Explain how to label busbars and borders when they resemble or intersect a target feature, rather than relying on annotators to infer the intended rule. Keep the examples tied to the actual modality and image conditions in the dataset.
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The Jülich dataset supplies defect probability and cell type for each cell image. Those are cell-level labels; they do not reveal the exact defective pixels. By contrast, semantic segmentation requires masks that identify target pixels. Chen and colleagues’ crack-segmentation work uses masks for segmentation and reports crack and busbar segmentation. Treating a cell probability as a pixel mask would fabricate spatial precision that the label does not contain.
If weak or cell-level labels are used to generate provisional pixel annotations, identify them as pseudo-labels or derived annotations, retain their origin, and do not present them as manually verified ground truth. A project may use such labels for pretraining or prioritizing review, but evaluation should distinguish them from masks created or checked under the project’s pixel-level policy.
How to review masks and prevent dataset leakage
Begin with a pilot batch rather than labeling the entire collection at once. Have the team apply the written guide, compare disagreements, and update unclear rules before production. During labeling, retain raw annotations separately from reviewed masks so that adjudication does not erase the original decisions. Periodic quality checks should include both routine examples and difficult cases, with a documented process for resolving inconsistent labels.
For data derived from complete modules, split train, validation, and test sets by source module where feasible, rather than randomly distributing cell crops. Cells from one module can share acquisition conditions and visual characteristics; putting related cells on both sides of an evaluation split can make the test set less independent. This is a methodological recommendation based on grouped data structure, not a requirement stated by the cited dataset records. Record the split unit and the identifiers assigned to each partition so the evaluation can be reproduced.
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Report the mask definition, class balance, modality, acquisition conditions, preprocessing, split unit, and evaluation-set construction alongside scores. For segmentation, provide per-class intersection over union (IoU) and precision/recall or F1, rather than a single aggregate alone. State whether the reported score is from a held-out test set and what images or modules it contains. Scores from different papers are not directly comparable unless their datasets, capture conditions, annotation conventions, and split designs are sufficiently alike.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published datasets and results can—and cannot—tell you
| Resource or result | What is reported | How to use it |
|---|---|---|
| Jülich EL Dataset, version 1.5 record | 2,624 cell crops at 300 × 300 pixels, 8-bit grayscale, from 44 PV modules; defect probability and cell type are supplied per cell image. | Useful for understanding processed EL cell images and cell-level labels. The probability labels are not pixel masks. |
| Sandia PV-IV-EL catalog | 613 corresponding IV/EL measurement sets and 438 unique modules across 28 models and 17 brands; the catalog connects EL images with IV traces and metadata including acquisition parameters and outdoor exposure. | Useful as an example of preserving module provenance and linked measurement context. Do not assume every record has complete metadata. |
| Chen et al., 2023 crack segmentation | Reports a VGG16-encoder U-Net, automated cell cropping, and crack and busbar segmentation. Its average test F1 was 0.875 and IoU was 0.782 on its own test set. | Use as a study-specific example of reporting segmentation metrics. These scores are not expected results for a different dataset or capture setup. |
| Scalable annotation framework preprint, 2022 | The abstract reports a 60% reduction in annotation cost on its evaluated validation task. | Treat as a paper-specific result, not a general labor-saving estimate. The reported percentage does not establish the expected cost reduction for another project. |
| NREL visual inspection collection tool, 2012 | A field-condition documentation tool with 14 sections, evaluated on more than 60 modules from more than 20 manufacturers at two sites. | It can inform field-inspection documentation; it is not a segmentation-mask standard. |
A separate NREL report from 2025 covers EL machine-learning defect detection and UV-fluorescence demonstrations. Those subjects can help frame other inspection approaches, but they do not make UV fluorescence, EL, and outdoor IR interchangeable modalities.
Common failure modes and practical fixes
Image and mask no longer line up
This often follows a crop, resize, rotation, or perspective correction applied to only one file. Re-run spatial transforms from the same recorded parameters for both image and mask, then inspect a mask overlay at the original and final dimensions. Check that class IDs survive resizing; use a mask-resampling method that preserves discrete labels rather than creating unintended intermediate classes.
Annotators disagree on thin or ambiguous defects
Do not solve disagreement by silently averaging different interpretations. Identify the exact boundary rule or uncertainty case that caused the mismatch, update the guide with an example, and adjudicate affected labels consistently. If a feature cannot be resolved in the source image, mark it according to the project’s uncertainty policy rather than claiming a precise boundary.
Cell labels are being used as segmentation ground truth
Check the annotation granularity before training. A class or probability attached to an entire cell supports cell-level tasks, not a claim about which pixels are defective. Obtain pixel masks or explicitly treat generated spatial labels as provisional derived annotations.
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Test performance appears implausibly strong
Review the split for repeated modules, neighboring crops, or duplicate source imagery across partitions. Split by module when possible and report that unit. Also confirm that masks or transformed versions of the test images were not included in training data.
Corrections make the dataset hard to reproduce
Restore the untouched input, preserve transformation parameters, and version the preprocessing code and labeling guide. Store reviewed masks separately from raw annotations, with an audit trail that records which labels changed and why.
Choosing an appropriate quality report
A compact dataset report should let another team answer four questions: what did the camera observe, what did annotators call a defect, how were pixels transformed and labeled, and what exactly was held out for evaluation? Give per-class results because performance can differ across defect types; include IoU and precision/recall or F1 with the test-set definition. If comparing a new result with a published one, explain differences in modality, sample unit, module provenance, annotation rules, and split design instead of presenting the numbers as a like-for-like ranking.
Keep the scope explicit when summarizing results. A metric from EL cell crops does not establish performance on whole-module EL images, outdoor IR scans, another camera setup, or a different defect taxonomy. The reported F1 of 0.875 and IoU of 0.782 belong to the test set and method in Chen et al.’s 2023 paper, not to solar-panel segmentation generally.
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