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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsChoose the smallest input representation that still preserves the smallest actionable defect or boundary, then give each tile enough surrounding context for classification. There is no universally correct image dimension or tile size. Test a small, documented grid on source-separated validation data and select the setting that balances small-defect recall, IoU or Dice, boundary quality, stitching artifacts, and memory or latency on the device you will deploy.
First identify the imaging modality. In aerial or satellite work, “resolution” usually means ground sampling distance (GSD), such as 0.8 m or 0.1 m per pixel. In electroluminescence (EL) and thermal inspection, it usually means the native raster dimensions and the crop or resize applied before inference. Those quantities are not interchangeable, and a tile size that works for aerial panel footprints should not be transferred mechanically to cell-level defect images.
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Start by defining the segmentation problem
Input choices depend on what the mask represents. A model segmenting complete photovoltaic (PV) panel footprints in aerial imagery sees a different scale and context requirement from a model segmenting cracks, inactive cells, or other defects inside an EL image.
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PV-footprint segmentation from aerial or satellite imagery
For aerial data, record the sensor’s GSD and the physical size of the panels or arrays. A 0.1 m image resolves much finer structure on the ground than a 0.8 m image, even if both are later resized to the same raster dimensions. Tile dimensions therefore have to be interpreted together with GSD: a 1,024-pixel tile covers a different physical area at each GSD.
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Defect segmentation in EL or thermal images
For EL and thermal inspection, record the native sensor raster, lens and acquisition setup, and every crop or resize operation. A 640 × 590 EL image and a 512 × 640 UAV-thermal image may contain different defect scales and contrast patterns. Resizing either to a common network input does not make the underlying data equivalent.
Write down the output and the smallest actionable target
Specify whether the output is a binary panel mask, a cell-level defect mask, or several defect classes. Then define the smallest defect that must remain actionable after preprocessing. This target, rather than a fashionable input size, is the anchor for resolution selection.
What published datasets actually show
Existing studies demonstrate scale dependence, not a universal recipe. The following values are documented settings from named datasets and experiments.
| Study or dataset | Imagery and scale | Documented tile or image size | Reported result or statistic |
|---|---|---|---|
| Jiang et al., 2021, multi-resolution aerial and satellite PV dataset | 0.8 m GSD for concentrated utility-scale PV | 1,024 × 1,024 pixels (PV08) | IoU 0.845 in the study’s recommended PV08 application setting |
| Jiang et al., 2021 | 0.3 m GSD for distributed ground PV | 1,024 × 1,024 pixels (PV03) | IoU 0.871 in the study’s recommended PV03 application setting |
| Jiang et al., 2021 | 0.1 m GSD UAV imagery for fine-grained rooftop PV | 256 × 256 pixels (PV01) | IoU 0.868 in the study’s recommended PV01 application setting |
| MSPS Dataset repository, 2026 | EL images of monocrystalline silicon cells | 640 × 590 pixels; 1,500 images | Mean defect footprint 128 pixels²; range 1–821 pixels²; approximately 0.05% of pixels are defect pixels |
| Pratt, Mattheus and Klein, 2023 | EL cell and module imagery with semantic masks | 593 cell images; dimensions not stated in the cited summary | IoU was stronger for larger features than for smaller defects |
| MFPN-ASPP study, 2026 | UAV thermal PV imagery | 1,009 images, each 512 × 640 pixels | Study-specific segmentation experiment; no universal tile recommendation established |
Jiang et al. used 3,716 PV samples across the multi-resolution groups. Their results show why a setting must be tied to physical scale and application: 1,024-square tiles were used for the 0.8 m and 0.3 m groups, while the 0.1 m group used 256-square tiles. Those are documented choices for that dataset, not defaults for another camera, geography, or model.
The MSPS statistics illustrate the opposite challenge. A mean defect area of 128 pixels² can conceal many extremely small targets: the reported range extends down to 1 pixel², while defect pixels account for only about 0.05% of the image. Downsampling can erase such targets or make them indistinguishable from background, even when an aggregate score looks acceptable.
A practical selection procedure
1. Record the acquisition and preprocessing chain
Create a one-page specification for every source:
- Modality: satellite RGB, aerial RGB, UAV thermal, EL, or another sensor.
- Native raster dimensions and aspect ratio.
- GSD for aerial or satellite imagery, including whether it varies between flights or scenes.
- Crop coordinates, resize method, normalization, bit depth, and any denoising or contrast operation.
- Target classes and the smallest defect or boundary that affects a decision.
- Model input shape, stride, receptive-field assumptions, and deployment memory limit.
This prevents a common mistake: calling both a 0.1 m GSD scene and a 256 × 256 network input “resolution” without saying which one is meant.
2. Measure the smallest target in native pixels
Use annotated training or pilot images to measure the width, height, and area distribution of the smallest defects that matter. Keep the native-resolution images as a baseline. For EL data, the MSPS range of 1–821 pixels² is a warning that a mean area is not enough; the lower tail determines whether resizing is safe.
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For aerial panels, measure the narrowest panel edge, gap, or boundary that must be separated. Convert physical dimensions to pixels using the recorded GSD. If a target occupies only a few native pixels, a downsampled version may remove its shape even when the panel itself remains visible.
3. Build a small candidate grid
Do not search every possible size. Start with the native representation and a few deliberately chosen alternatives:
- Native or minimally resized input, to establish the maximum available detail.
- A moderate downsample, to test whether memory and throughput improve without losing small targets.
- A crop or tile size that increases context while retaining the target at adequate pixel scale.
- If the model requires fixed dimensions, adjacent sizes that satisfy its stride constraints.
Keep the candidate grid small enough that every configuration receives the same training budget and validation protocol. The purpose is to identify the failure tradeoff, not to manufacture a single “optimal” number.
4. Match tile coverage to context and receptive field
A tile should contain the target plus enough surrounding scene to distinguish it from similar structures. For aerial panels, context may include neighboring panels, roof geometry, roads, or vegetation. For EL and thermal defects, context can include adjacent cells, busbars, module borders, and normal texture.
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More pixels are not automatically better. A very large tile can make a small defect occupy a tiny fraction of the input and may exceed GPU memory. A very small tile can cut through a panel, cell, or defect and deprive the model of context. Compare physical coverage as well as pixel dimensions when GSD differs.
5. Keep data splits independent
Split by source image, module, site, or acquisition session wherever possible. Adjacent tiles from one original frame can be near-duplicates; putting some in training and others in validation makes a configuration appear stronger than it will be on a new module or flight. The exact split rule is project-specific, but the principle is fixed: validation must represent the deployment background and target distribution.
6. Train and evaluate every candidate consistently
Hold architecture, augmentation policy, class weighting, optimizer schedule, and stopping rule constant while comparing input settings. Otherwise you are measuring several changes at once. Save the complete configuration, including tile dimensions, overlap, padding, interpolation, batch size, and checkpoint used for evaluation.
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Metrics that expose resolution and tiling failures
Overall accuracy is particularly misleading when defect pixels are rare. Use metrics that reveal whether the smallest targets and their boundaries survive preprocessing.
| Measure | What it reveals | How to use it |
|---|---|---|
| Per-class recall | Missed defects or panel classes | Report separately for each defect type and for the smallest-size bin, not only as a global average. |
| IoU and Dice | Mask overlap | Compare per class and by target-size group; small objects can disappear inside a good aggregate score. |
| Boundary quality | Jagged, shifted, or incomplete edges | Inspect boundary precision and visual overlays, especially where panels or cells touch similar backgrounds. |
| Stitched-output inspection | Tile seams and truncated objects | Review full images after reassembly, not just individual tile predictions. |
| Latency, throughput and memory | Deployment feasibility | Measure on the intended device with the production batch size and preprocessing pipeline. |
The 2023 EL benchmark reported stronger IoU on larger features than on smaller defects. Use that pattern as a diagnostic: if a candidate setting improves the large-object score while small-defect recall falls, it is not an improvement for a defect-inspection workflow.
Inference tiling, overlap and stitching
Choose overlap to protect boundaries
When a full image does not fit in memory, divide it into tiles with overlap. Overlap gives a defect or panel edge a chance to appear away from a tile boundary, where the model has complete context. The required overlap depends on object size, model receptive field, and the quality of the edge predictions; it should be measured rather than assumed.
Define padding and edge behavior
Document whether incomplete edge tiles are padded by reflection, replication, zeros, or another method. Padding changes the visual context and can create false patterns in EL or thermal images. Apply the same policy during validation and deployment.
Blend predictions before reassembly
Record how overlapping masks are combined: averaging logits, averaging probabilities, selecting the center crop, or another rule. Inspect seams on full-resolution outputs. A candidate that scores well on isolated tiles can still produce broken boundaries when neighboring predictions disagree.
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Create a validation subset in which panels, cells, and defects intersect tile edges. Compare recall and boundary continuity for those cases against objects centered in a tile. This directly tests whether the chosen tile and overlap protect the failure mode you care about.
Memory and speed trade-offs
For a fixed number of channels, increasing both tile dimensions increases the number of input pixels approximately with the square of the side length. Larger tiles can supply more context but normally require more activation memory and reduce the batch size that fits. Smaller tiles can raise throughput while increasing the number of windows, overlap calculations, and stitching operations.
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Benchmark the complete pipeline, not only model forward time. Include image decoding, resizing, normalization, tile extraction, transfer to the accelerator, inference, and stitching. Record batch size, precision mode, peak memory, latency per image, and throughput.
The MSPS repository attributes its reported throughput to an NVIDIA RTX 3090 Ti. Treat that as a hardware-qualified result, not an expected speed on a laptop GPU, embedded accelerator, or CPU. A tile setting that fits training hardware may still fail the memory or latency budget of the inspection system.
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Resolution changes are a form of domain shift. A model trained on one GSD, camera, focus level, thermal sensitivity, or EL acquisition setup may not preserve its defect features when the source changes.
Jiang et al. reported that direct cross-application between samples with different resolutions performed poorly and that fine-tuning with target samples was necessary in their experiments. Their conclusion is evidence for validating and adapting on target imagery, not a claim that every architecture fails identically.
When the deployment source changes
- Collect representative target-domain images before locking the input size.
- Measure the smallest target again in the new native pixels or GSD.
- Run the existing model without adjustment to establish the transfer gap.
- Fine-tune with labeled target samples when the gap is material.
- Recheck per-class recall, small-target IoU or Dice, boundaries, and stitched outputs.
When only the network input changes
If acquisition stays fixed but the model input is resized, compare predictions against the native baseline. Keep interpolation and aspect-ratio handling explicit; stretching a 640 × 590 image to a square changes defect geometry, while letterboxing changes the effective pixel distribution and introduces padding.
A comparison worksheet for your validation run
Use one row per configuration so the final choice is auditable.
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| Field | Configuration A | Configuration B | Configuration C |
|---|---|---|---|
| Modality and source | Record exactly | Record exactly | Record exactly |
| Native GSD or raster | Record exactly | Record exactly | Record exactly |
| Tile and model input | Record dimensions | Record dimensions | Record dimensions |
| Smallest-target visibility | Measure or describe | Measure or describe | Measure or describe |
| Overlap, padding and blend rule | Record exactly | Record exactly | Record exactly |
| Per-class recall | Report values | Report values | Report values |
| Per-class IoU or Dice | Report values | Report values | Report values |
| Boundary and seam review | Passes and failures | Passes and failures | Passes and failures |
| Peak memory and latency | Named hardware | Named hardware | Named hardware |
| Robustness across sites or devices | Report split results | Report split results | Report split results |
Choose the configuration that meets the operational minimums for the smallest actionable class and boundary quality, then fits the deployment budget. Do not let a small gain in average IoU justify losing the defects the system exists to find.
Common mistakes and fixes
Copying a published tile size
Symptom: A 1,024-square or 256-square input is adopted because it appears in a paper. Fix: Recalculate physical coverage, target pixel size, and memory for your modality and GSD. Jiang et al.’s values are tied to their PV08, PV03, and PV01 groups.
Confusing GSD with network dimensions
Symptom: Two images resized to 512 pixels are treated as having the same detail. Fix: retain the native raster and GSD metadata, and report both whenever comparing experiments.
Optimizing only global IoU
Symptom: The score rises while visual inspection shows missed tiny defects. Fix: add size-binned recall and IoU or Dice, then review false negatives and boundaries.
Allowing train-test tile leakage
Symptom: Validation is excellent but performance drops on a new module or site. Fix: split by source image, module, site, or session rather than by randomly shuffled neighboring tiles.
Ignoring stitching artifacts
Symptom: Full-image masks contain seams or discontinuities. Fix: test overlap, padding, and blending on objects that cross tile edges, and judge the reassembled image.
Quoting speed without hardware
Symptom: A reported throughput cannot be reproduced. Fix: state accelerator or CPU, precision, batch size, image dimensions, and whether preprocessing and stitching are included.
Decision rules by modality
For aerial or satellite panel footprints
- Start from GSD and the physical panel or roof features that must be separated.
- Keep physical coverage comparable when testing different raster sizes.
- Use site- and acquisition-separated validation to expose background shifts.
- Consider target-domain fine-tuning when GSD or sensor changes.
For EL cell or module defects
- Keep a native 640 × 590-style baseline when that is the source raster, rather than assuming a square crop is harmless.
- Quantify the lower tail of defect areas; the MSPS data include defects as small as 1 pixel².
- Evaluate small-defect recall separately because rare pixels can be overwhelmed by normal-cell background.
- Check that resizing and padding do not create artificial bright or dark patterns.
For UAV thermal inspection
- Record the 512 × 640-style native aspect ratio, flight conditions, and thermal acquisition settings.
- Validate across modules, backgrounds, and flights instead of treating one thermal scene as representative.
- Inspect both defect contrast and mask continuity after stitching.
The defensible answer
The correct input resolution and tile size are the pair that preserve your smallest actionable target, provide sufficient context, and satisfy measured deployment limits on representative held-out imagery. Published examples—0.8 m, 0.3 m, and 0.1 m GSD with 1,024- or 256-pixel tiles, tiny EL defects in 640 × 590 images, and 512 × 640 thermal frames—show how strongly the choice depends on modality and scale. Treat them as comparison points, not prescriptions. Document the candidate grid, source-separated split, small-target metrics, stitching behavior, and named hardware so another engineer can reproduce the decision.
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