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Small cracks in photovoltaic electroluminescence (EL) images are missed mainly because they occupy very few pixels against a much larger background and larger cell features. A model can achieve strong overall accuracy while ignoring thin crack branches, breaking one crack into fragments, or predicting a broad but poorly aligned mask. The practical remedy is not one magic architecture: define labels consistently, preserve spatial detail, measure crack-specific recall and overlap, compensate for foreground imbalance, and validate on images from the cameras and production batches where the model will run.
The most revealing warning sign is a high crack recall paired with low IoU. In a 2023 benchmark, DeepLabv3+ with custom class weights reached 86% crack recall but only 25% crack mIoU. The model found many crack pixels, yet its boundaries and continuity were often wrong. The workflow below shows how to diagnose that pattern and improve it without mistaking a dataset-specific result for a universal guarantee.
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Why small photovoltaic cracks are difficult segmentation targets
Foreground pixels are overwhelmed by background
Semantic segmentation assigns a class to every pixel. In an EL image, a crack may be a one- or few-pixel-wide structure, while the cell background, busbars, fingers and other features cover most of the frame. If the loss and sampling strategy treat every pixel equally, the optimizer can reduce its error by learning the abundant background and larger structures. Missing a narrow crack then has little effect on the aggregate loss.
This imbalance also makes headline accuracy misleading. A mask that labels nearly every background pixel correctly can score well overall while omitting a clinically important crack. The 2023 benchmark by Pratt and colleagues evaluated 593 labeled cell images with pixel-level labels for 24 defects and features, using 50 test images. Its larger feature classes achieved higher overlap than smaller defect classes, illustrating how object size affects segmentation quality.
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Downsampling erases thin structures
Most encoder-decoder networks reduce an image to lower-resolution feature maps before reconstructing a mask. A crack that survives at the original scale can disappear when several neighboring pixels are pooled, resized or compressed. Repeated downsampling can also remove short branches and disconnect segments that should form one continuous line. Upsampling the coarse prediction cannot recover detail that was discarded earlier.
Low contrast and variable scale hide the signal
Production EL images can contain low-contrast defects, irregular boundaries and cracks at different scales. The MSPS-Dataset repository describes 1,500 production-line images at 640 × 590 pixels, with minute targets, low contrast, scale variation, irregular boundaries and severe foreground-background imbalance. Those conditions make a model sensitive to camera exposure, optics, electrical stimulation and preprocessing choices. A crack visible to an inspector may occupy only a subtle intensity change for the network.
Labels are not perfectly objective
Thin branches, faint endpoints and intersections can be ambiguous. One annotator may trace the visible centerline, another may include a wider halo, and a third may omit a barely visible segment. Inconsistent masks teach the model conflicting boundaries and can make a correct prediction appear wrong during evaluation. Before changing the architecture, document what counts as a crack, how branches and uncertain pixels are handled, and whether all annotators follow the same rule.
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Several recent studies quantify the problem, but each result belongs to a specific dataset, split and label protocol. Use the figures as diagnostic evidence rather than as performance promises for a new camera or plant.
| Source and year | Data or protocol | Reported finding | How to interpret it |
|---|---|---|---|
| Pratt et al., Systems and Soft Computing, 2023 | 593 labeled cell images; 24 defect and feature classes; 50 test images | DeepLabv3+ with custom class weights: 86% crack recall and 25% crack mIoU | Recall and boundary overlap can diverge sharply; larger classes were easier than smaller defects. |
| PV-S3, Engineering Applications of Artificial Intelligence, 2025 | UCF-EL experiment using 20% labeled samples and unlabeled EL data | Against its supervised comparison, absolute gains of 9.7 percentage points in mIoU, 13.5 in precision, 29.15 in recall and 20.42 in F1 | Evidence that semi-supervised learning can help when labels are scarce under that study’s protocol; gains are not guaranteed elsewhere. |
| MultiSolSegment, Solar Energy, 2026 | 2,340 labeled monocrystalline-silicon images; multi-channel segmentation of overlapping EL features | Reports a newer multi-channel U-Net direction | Relevant for overlapping features, but the publication does not establish a universally best architecture for small cracks. |
| MSPS-Dataset repository, accessed 2026 | 1,500 images at 640 × 590 pixels; 1,000 for training and 500 for testing | Production-line EL crops with minute, low-contrast, multiscale and irregular defects | Useful evidence about acquisition difficulty; repository facts are not a peer-reviewed deployment benchmark. |
Diagnose the failure before changing the model
Report crack-specific metrics
At minimum, report crack recall, precision and IoU or Dice for the crack class. Recall answers how much labeled crack area was found; precision measures how much predicted crack area is correct; IoU and Dice expose overlap quality. Include the number of images and pixels used for each calculation, because a pooled pixel score can hide a few badly segmented images.
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Also report a confusion breakdown: false-negative crack pixels, false-positive pixels on intact cell regions, and missed connected components. A model that finds every crack but draws many spurious lines has a different engineering problem from one that produces clean masks but misses faint branches.
Stratify by crack size
Define size bins using a reproducible measure such as labeled crack pixels, skeleton length or bounding-box area. Report recall and IoU for each bin, not only one average. If the smallest bin collapses while larger cracks perform well, the issue is genuinely scale-related rather than a general training failure. Keep the bin definitions fixed between experiments so that a change in the reported score reflects the model, not a new measurement rule.
Inspect masks at native resolution
Review the original EL image, the ground-truth mask and the prediction side by side at the evaluation scale. Zoom into endpoints, branches, crossings and low-contrast regions. Look for systematic patterns: cracks disappearing near cell edges, gaps after a resize operation, masks that are consistently too wide, or false positives along busbars. Save representative false negatives and false positives for every training run; a single mean score cannot show these failure modes.
Fix the data and label definition first
Create an explicit crack annotation policy
- Define whether a crack includes only the visibly dark or bright core, a surrounding affected region, or both.
- Specify how to mark one-pixel branches, faint endpoints, crossings and cracks that touch a cell boundary.
- Decide how ambiguous pixels are handled: assign them to crack, background or an ignore label consistently.
- Have annotators label a shared sample, review disagreements and revise the policy before producing the final training split.
- Keep an untouched validation and test set whose masks are reviewed under the same policy.
Consistent labels do not make every boundary certain, but they prevent the model from being penalized for incompatible instructions. If two masks legitimately represent different interpretations, record that uncertainty instead of silently treating one as an unquestionable ground truth.
Prevent leakage between related images
Images from the same module, cell, camera session or production batch can be highly correlated. Split by the appropriate physical unit rather than randomly scattering near-duplicates across training and test sets. Otherwise, a model may appear to recognize tiny cracks because it has seen nearly identical illumination or cell texture during training.
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Preserve spatial detail during training and inference
Audit every resize and crop
Record the original dimensions, the dimensions fed to the network and the final dimensions used for scoring. Compare predictions at the original evaluation scale. If a resize turns a narrow crack into a subpixel feature, no loss function can restore the lost evidence. When memory is limited, evaluate tiled or cropped inference, but ensure that tiles retain enough surrounding context and that overlapping tiles are merged without creating seams.
There is no universally optimal tile size established by the cited studies. Treat tile dimensions, overlap and interpolation as experiments. Measure the smallest crack widths that remain represented after preprocessing and reject a configuration that removes them before the model sees the image.
Keep labels aligned with transformations
Every geometric transformation applied to an EL image must be applied identically to its mask. Use nearest-neighbor handling for categorical masks so that interpolation does not create artificial fractional crack labels. Verify alignment by overlaying transformed masks on the transformed image before launching a long training run.
Compare native-scale and detail-preserving baselines
Establish one reproducible baseline, then change one spatial factor at a time: input resolution, crop strategy, encoder output stride or decoder detail path. Keep the data split, random seed policy and metrics fixed. An improvement is meaningful only if it survives the same held-out evaluation and does not simply trade missed cracks for noisy predictions.
Use class-aware objectives, then tune for precision and boundaries
Why weighting helps
Class weights increase the loss contribution of sparse crack pixels, giving the optimizer a stronger signal when a prediction misses them. The Pratt benchmark found improved results for selected defects with custom class weights, and the SEiPV-Net study in Energies reported better visual results after weighting while noting that micro-defect segmentation remained imprecise.
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Weighting is therefore an experiment, not a guaranteed cure. Excessive crack weight can produce halos, random streaks and over-wide masks. Select weights on a validation set and monitor precision, IoU or Dice, connected-component continuity and boundary quality alongside recall.
Use a controlled tuning procedure
- Train an unweighted baseline and record crack recall, precision, IoU or Dice, plus size-stratified results.
- Test a small range of class-weight settings while holding preprocessing and architecture constant.
- Choose the operating point based on the cost of a missed crack versus a false alarm in your inspection workflow.
- Review boundary overlays and connected components, not only the scalar metric that improved most.
- Freeze the selected setting and evaluate once on the untouched test split.
If your production system converts masks into repair decisions, calibrate the probability threshold on validation data rather than assuming the default threshold is appropriate. Report the chosen threshold with the model so that later comparisons remain reproducible.
Use unlabeled EL images when annotation is the bottleneck
Large collections of EL images may exist even when pixel-level crack masks are expensive. Semi-supervised training can use a smaller labeled set together with unlabeled images, provided the validation and test images remain isolated. The 2025 PV-S3 study on UCF-EL used 20% labeled samples and reported absolute improvements over its supervised comparison of 9.7 percentage points in mIoU, 13.5 in precision, 29.15 in recall and 20.42 in F1.
Those gains describe that dataset and protocol. To test whether the approach transfers, repeat the comparison with the same held-out split, the same preprocessing and the same labeled-image fraction. Annotate a representative validation set that includes faint, short and branched cracks; otherwise an unlabeled-data method can look successful while improving only easy examples.
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Encoder-decoder models
DeepLabv3+ is a useful baseline because it can combine context with a decoder for spatial recovery, but its 25% crack mIoU in the cited benchmark shows that a capable general segmentation architecture does not automatically solve tiny defects. Treat the benchmark configuration, class weights and split as part of the result; changing any of them changes the comparison.
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U-Net and multi-channel designs
MultiSolSegment, published in Solar Energy in 2026, reports a multi-channel U-Net for overlapping EL-image features using 2,340 labeled monocrystalline-silicon images. Such a design may be relevant when several visual features overlap or when additional image channels carry complementary information. The reported dataset does not prove that it will outperform another model on a different module type, camera or crack definition.
Make comparisons reproducible
- Use the same image-level split and prevent related cells or modules from crossing splits.
- Keep preprocessing, augmentation, label policy and probability threshold fixed when comparing models.
- Report crack recall, precision, IoU or Dice, boundary quality, size-stratified results and false-positive burden.
- Record inference resolution, hardware and batch settings when latency matters.
- Release the split definition and evaluation script internally so later experiments cannot change the denominator unnoticed.
Validate across the acquisition domain
A model trained on one EL camera or production line can fail when contrast, resolution, electrical excitation, module construction or image cropping changes. Hold out images from distinct acquisition conditions or production batches. Keep the test set untouched until the design is locked, then inspect errors by crack size, contrast, orientation, cell region and image source.
Domain validation should include the smallest defects that matter operationally. If a new batch has lower contrast and thinner apparent cracks, measure those cases separately rather than hiding them inside an overall average. A model that performs well on large, high-contrast cracks may still be unsuitable for early-warning inspection.
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Recall is calculated from the crack pixels that truly exist: it rises when the prediction captures more of those pixels. IoU divides the intersection of prediction and ground truth by their union, so extra pixels, misplaced boundaries and disconnected strokes reduce the score. A prediction can therefore cover most of a crack while also spreading beyond it, bridging separate cracks or missing exact endpoints.
The benchmark result of 86% recall and 25% crack mIoU is a concrete example. It does not mean the recall is wrong; it means the two metrics answer different questions. Use recall to monitor missed defect area, and IoU or Dice plus visual boundary review to determine whether the mask is usable for measurement, localization or downstream automation.
Troubleshooting guide
| Observed symptom | Likely cause | Checks and corrective action |
|---|---|---|
| Large cracks segment well; short cracks disappear | Scale loss, downsampling or insufficient small-target examples | Inspect post-resize crack width, preserve native detail, add size-stratified reporting and test higher-resolution or tiled inference. |
| Recall rises but IoU and precision fall | Overweighting the crack class or using an unsuitable threshold | Reduce or retune the weight, select the threshold on validation data and inspect false-positive components and boundaries. |
| Predictions are fragmented | Branches lost during pooling, tile seams or inconsistent labels | Overlay masks at native scale, inspect tile overlap and merge logic, and review branch annotation policy. |
| False positives follow busbars or cell edges | Background structures resemble cracks or the model learned camera-specific patterns | Review hard negatives, split by acquisition batch and test on a separate camera or production condition. |
| Validation is strong but a new batch fails | Domain shift in contrast, resolution, module type or preprocessing | Measure the new batch separately, verify image normalization and retrain or adapt only after documenting the shift. |
| Different annotators produce incompatible scores | Unclear crack and boundary definitions | Adopt a written policy, adjudicate a shared sample and use an ignore rule for genuinely ambiguous pixels. |
A practical end-to-end checklist
- Define the target: document crack width, branches, endpoints, overlaps, boundaries and ambiguous pixels.
- Build a leakage-safe split: keep related cells, modules and batches together.
- Train a baseline: save predictions, thresholds, preprocessing and random-seed settings.
- Measure what matters: report crack recall, precision, IoU or Dice, false negatives, false positives and size-stratified performance.
- Inspect failures: review native-resolution overlays for short, faint, branched and edge-touching cracks.
- Protect detail: test resizing, tiling and output stride for evidence loss before changing the network.
- Tune imbalance: compare class weights or other class-aware losses while monitoring precision and boundaries.
- Use unlabeled data carefully: reproduce the PV-S3-style comparison with a representative labeled validation set.
- Compare architectures fairly: hold split, preprocessing, labels and metrics constant.
- Stress-test deployment: evaluate distinct cameras, batches, module types, contrast levels and crack sizes before release.
What a defensible result looks like
A credible claim is specific: for example, a model achieved a stated crack recall and IoU on a named held-out split, with size-stratified results and a documented label policy. It identifies the image resolution, acquisition conditions, threshold and whether the score is pooled over pixels or averaged per image. It also shows representative false negatives and false positives.
A single aggregate score, even a high one, is not enough for small-crack inspection. The cited benchmark, semi-supervised study, multi-channel U-Net work and production-line dataset all point to the same engineering principle: performance depends on the interaction between target size, labels, image formation, preprocessing, loss, architecture and evaluation protocol. Fix the weakest link you can measure, then retest on the domain where the system will operate.
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