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Build the pipeline as a binary semantic-segmentation system for thermal anomalies: normalize each 512×640 image, feed it through a MobileNetV2 encoder, fuse multiscale features with a feature pyramid network (FPN), aggregate context with atrous spatial pyramid pooling (ASPP), and convert the output probabilities to a mask at a 0.5 threshold. The architecture reported by Galal et al. combines those stages with augmentation, a Binary Cross-Entropy plus Dice loss, validation during training, and Dice/IoU testing. Its published results—mean Dice 0.8440 and average IoU 0.7564 over five runs—come from one dataset and should be treated as study-specific evidence, not a guaranteed field result.
What this pipeline segments
The target is binary pixel segmentation of anomalous regions in photovoltaic (PV) thermal images. For every pixel, the model estimates whether it belongs to an anomaly or to the background. The resulting mask can support inspection workflows such as highlighting hotspots for human review, but it is not, by itself, a complete diagnosis of every PV failure mode. A thermal anomaly may require electrical measurements, visual inspection, operating-history checks, or an engineer’s judgement.
The design described in the 2026 Scientific Reports paper by Galal, Marzouk, Moustafa, Nafea and colleagues is called MFPN-ASPP. It combines three ideas:
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- FPN: a top-down feature pyramid that restores fine spatial information by combining deep semantic maps with shallower maps.
- ASPP: parallel convolutions with different dilation rates plus global context, allowing the decoder to consider defects at multiple spatial scales.
The complete workflow is: prepare and split the images and masks, normalize thermal values, apply training-only augmentation, extract features, fuse them, aggregate context, predict a probability for each pixel, validate during training, and evaluate a thresholded mask on held-out test images.
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Evidence and scope of the published result
The study uses the Photovoltaic Thermal Images Dataset. It contains 1,009 radiometric thermal images at 512×640 resolution, each paired with an expert-annotated binary mask. The images were collected from a 66 MW ground-mounted facility in Tombourke, South Africa, from 21–27 January 2019. Collection used a UAV-mounted radiometric thermal camera under clear, high-irradiance conditions. The authors describe access as research-request based through a form that asks for the intended use.
Galal et al. report a mean Dice score of 0.8440 and an average IoU of 0.7564 over five runs. In their experiment, they also report higher scores than the U-Net, LinkNet, FPN, Mask-RCNN and YOLO-based variants they tested. Those comparisons apply to that paper’s split, labels, preprocessing and evaluation procedure. They do not establish the same ranking on another plant, camera, climate, image resolution or annotation policy.
The authors state: “A limitation of this study is that the proposed MFPN-ASPP model was evaluated on a single photovoltaic thermal image dataset.” They call for testing additional datasets, sensor configurations and operating conditions, including analysis by defect size. Therefore, before maintenance decisions depend on the output, hold out data from the intended site and camera and measure performance under the conditions in which the system will operate.
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Keep the thermal image information consistent
Preserve the study’s 512×640 image dimensions when reproducing its setup. Convert the thermal values to a numeric tensor and normalize them to the [0, 1] range. Use one documented normalization rule for training, validation and testing. If a new camera supplies a different radiometric range, determine its calibration and normalization from that camera rather than silently reusing the old range.
Store each image with an unambiguous match to its mask. A practical record contains an image identifier, image path, mask path, capture time, site or flight identifier, and camera identifier. Keep those metadata fields even if the model does not consume them; they are needed to detect leakage and to diagnose failures later.
Split by acquisition unit, not just by random file name
Create non-overlapping training, validation and test sets before augmentation. Images from the same flight, panel string, scene or near-duplicate sequence should remain in one partition whenever possible. Otherwise, adjacent frames can place nearly identical thermal patterns in both training and test sets and make the score look better than performance on a genuinely new inspection.
The paper reports validation during training and test-time Dice and IoU, but the paper does not state its exact split proportions, batch size or epoch count. Do not invent those values when reproducing the work. Record your own split rule, random seed and sample counts so another team can repeat the evaluation.
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Check every mask before training
- Confirm that each mask has the same width and height as its image.
- Convert labels to a binary representation with a documented foreground value.
- Measure the number of positive pixels per image; images with no anomaly are valid but affect metric interpretation.
- Display random image-mask overlays and inspect very small, thin and boundary-touching regions.
- Remove or quarantine corrupted files before the split, rather than discovering them in a test run.
Apply the reported preprocessing and augmentation
The paper preserves the input resolution and applies augmentation during training. Its reported settings are empirical choices, not universal optima:
| Transformation | Reported setting | How to apply it safely |
|---|---|---|
| Horizontal flip | Probability 0.5 | Apply the identical flip to image and mask. |
| Brightness/contrast adjustment | Probability 0.5 | Adjust the image only; keep the binary mask unchanged. |
| Shift | Up to 3% | Use the same geometric displacement for image and mask. |
| Scale | Up to 10% | Use mask-safe interpolation, such as nearest-neighbor, for labels. |
| Rotation | Up to 15 degrees | Use reflection padding for the geometric transform, as reported. |
Do not augment validation or test images. Geometric operations must transform the image and mask with exactly the same parameters; otherwise, the model is trained against incorrectly aligned labels. Intensity changes belong to the thermal image, not the mask. Tune the probabilities and ranges against validation data when moving to another site, and keep the test set untouched while making those choices.
Build the MobileNetV2–FPN–ASPP model
1. MobileNetV2 encoder
Use MobileNetV2 as the encoder and expose feature maps from stages S2, S3, S4 and S5. Early stages retain more spatial detail, while deeper stages encode broader semantic context at lower resolution. The encoder should return all four maps, not only its final classification feature, because the FPN needs information at multiple scales.
Remove the classification head. For a segmentation model, the encoder output is a collection of feature tensors. If you use a framework implementation with pretrained weights, verify which internal blocks correspond to S2–S5 and document that mapping; stage names differ between libraries and versions.
2. Feature pyramid network
The FPN creates a top-down pathway. A 1×1 lateral convolution first puts each selected encoder feature into a compatible channel space. Starting at the deepest map, upsample by the required scale, add the lateral feature from the next shallower stage, and continue toward higher resolution. This combines semantic information from deep layers with localization detail from shallow layers.
In the reported design, the fused FPN representation has 128 channels. Keep the upsampling operation aligned with the target tensor dimensions; an off-by-one rounding difference can shift masks at image boundaries. After fusion, concatenate the pyramid features at a common resolution before sending them to ASPP.
3. ASPP context module
The concatenated pyramid representation passes through parallel ASPP branches. The reported dilation rates are 1, 6 and 12. The module also includes a 1×1 convolution branch and a global-average-pooling branch. These paths give the network local detail, dilated context and image-level context at the same time.
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The paper reports a 256-channel ASPP projection. Concatenate the branch outputs, project them to that width, and preserve the spatial dimensions needed by the segmentation head. A global-pooling branch must be resized back to the branch feature-map size before concatenation.
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4. Segmentation head and mask
Upsample the ASPP output to the original 512×640 resolution, then apply a 1×1 convolution with one output channel. A sigmoid converts that channel into a foreground probability for every pixel. During inference, classify pixels with probability at least 0.5 as foreground, as in the paper. Keep the continuous probability map as well as the binary mask: it is useful for threshold analysis and human review.
Implementation blueprint
The following framework-neutral sequence keeps tensor responsibilities clear. It is an implementation plan, not a claim that the paper used a particular software library or codebase.
image, mask = load_pair(id)
image = normalize_to_zero_one(image) # 512 x 640
if training:
image, mask = paired_geometric_aug(image, mask)
image = intensity_aug(image) # mask is unchanged
s2, s3, s4, s5 = mobilenet_v2_encoder(image)
p2, p3, p4, p5 = fpn_top_down(s2, s3, s4, s5, out_channels=128)
fused = concatenate_at_common_scale(p2, p3, p4, p5)
context = aspp(fused, dilation_rates=(1, 6, 12), projection_channels=256,
include_1x1=True, include_global_pool=True)
logits = segmentation_head(upsample(context, size=(512, 640)))
probability = sigmoid(logits)
loss = binary_cross_entropy_with_logits(logits, mask)
+ dice_loss(probability, mask)
mask_hat = probability >= 0.5In production code, handle tensors in batches, preserve the image’s height and width in metadata, and assert that the prediction and mask shapes match before calculating loss. If your library’s binary cross-entropy function expects probabilities rather than logits, use the corresponding form consistently; do not apply sigmoid twice.
Train with the combined loss
The reported training objective is Binary Cross-Entropy plus Dice loss. Binary Cross-Entropy penalizes per-pixel probability errors, while Dice loss emphasizes overlap between predicted and annotated foreground regions. A common Dice definition for a batch is:
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Dice = (2 × sum(p × y) + ε) / (sum(p) + sum(y) + ε)
Here, p is the predicted foreground probability, y is the binary mask and ε is a small numerical stabilizer. Use the same reduction convention throughout training and evaluation, and state whether empty-mask images are assigned a special score. The paper does not specify the paper’s optimizer, learning rate, batch size, epoch count, class weighting or early-stopping rule, so those are implementation decisions that must be logged and tuned on the validation partition.
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Monitor validation loss and validation Dice after each epoch. Save the checkpoint selected by a rule defined before looking at test results, such as the best validation Dice. Never use test masks to select an augmentation range, threshold, checkpoint or post-processing operation.
Evaluate masks with Dice and IoU
For binary masks, calculate true positives (TP), false positives (FP) and false negatives (FN) at the pixel level. Dice and intersection over union are:
- Dice:
2TP / (2TP + FP + FN) - IoU:
TP / (TP + FP + FN)
Report per-image values and an aggregate rule, such as the mean across images, because large anomalies can otherwise dominate a pixel-pooled score. Include the number of test images, the threshold, the split rule and the random seed. For a useful engineering assessment, add small-defect recall, boundary quality and false-positive rate, but label those as additional analyses; the cited paper’s headline evidence is its Dice and IoU results.
When comparing another model family—such as a plain U-Net, an FPN decoder without ASPP, or a detector-derived segmentation model—train and test every model on the same images, masks, augmentations and partition. Compare at least:
| Comparison axis | Why it matters | What to report |
|---|---|---|
| Overlap | Measures agreement with annotated regions. | Dice and IoU with confidence intervals or run-to-run variation. |
| Small-defect recall | Small anomalies can disappear in downsampling. | Recall stratified by annotated defect size. |
| Boundary quality | Maintenance review may depend on exact location. | A boundary metric or qualitative overlays. |
| Inference cost | UAV or field workflows may have limited compute. | Latency, memory and hardware configuration. |
| Cross-site generalization | Thermal appearance changes with camera and conditions. | Scores on a site or camera excluded from training. |
The cited study reports outperforming several named baselines in its own experiment, but it does not establish a universal winner across these axes or across other datasets.
Inference and visual quality checks
- Load the trained checkpoint and apply the exact test-time normalization used during training.
- Run the image through the encoder, FPN, ASPP and segmentation head without augmentation.
- Apply the 0.5 probability threshold to obtain the binary mask.
- Overlay the mask boundary and probability heatmap on the thermal image.
- Record the image identifier, model version, threshold, preprocessing version and timestamp with the output.
Inspect overlays rather than relying only on a scalar score. Look for masks that bleed across panel borders, miss narrow anomalies, activate on reflections or background structures, or change substantially when the image is slightly shifted. Preserve the raw probability map so an operator can review uncertain pixels instead of receiving only an irreversible yes/no result.
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Leakage inflates validation or test scores
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Image and mask no longer align
Symptom: training loss falls slowly and predictions have shifted edges. Fix: apply one geometric-transform object to both image and mask, use nearest-neighbor interpolation for labels, use reflection padding for the reported rotations and shifts, and render an overlay after every augmentation change.
Small anomalies vanish
Symptom: overall Dice is acceptable while tiny hotspots are missed. Fix: inspect the S2/S3 feature resolutions, verify FPN upsampling alignment, stratify validation by defect size and consider a threshold selected on validation data. Do not change the test threshold after seeing test masks.
Thermal ranges differ between cameras
Symptom: a model trained on one sensor produces saturated or low-contrast probabilities on another. Fix: verify radiometric calibration and normalization, measure the new camera’s value distribution and collect labeled validation images from its operating conditions. The single-facility study does not prove cross-camera robustness.
Empty masks distort the metric
Symptom: the reported mean changes greatly depending on a few normal images. Fix: publish the number of empty-mask samples and the rule used when both prediction and label are empty; report anomaly-containing and all-image subsets separately.
Boundary artifacts appear after resizing
Symptom: predictions contain halos or jagged edges near the image border. Fix: check reflection padding, interpolation modes and the exact sequence of upsampling operations. Compare tensor shapes at every FPN merge and crop or pad only with a documented rule.
Validate before using predictions for maintenance
Use the published architecture as a reproducible starting point, not as a field certification. A responsible deployment study should include images from the intended camera, plant layout, seasons, irradiance levels and flight patterns. Keep at least one site, flight period or camera configuration completely unseen during model selection. Have domain experts review false positives and false negatives, especially when a mask could trigger an inspection or shutdown.
Track dataset and model versions, annotation revisions, normalization parameters, augmentation settings, stage-to-feature mapping, threshold and hardware. Re-run the evaluation when the camera, radiometric processing, image resolution or operating procedure changes. If the model is used to prioritize work orders, define a human-review step and an escalation rule for low-confidence or out-of-distribution images.
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- It provides a specific binary thermal-image segmentation design: MobileNetV2 encoder, FPN fusion, ASPP context aggregation and a sigmoid mask head.
- It documents input normalization, the listed training augmentations, combined Binary Cross-Entropy plus Dice loss and a 0.5 inference threshold.
- It reports mean Dice 0.8440 and average IoU 0.7564 over five runs on 1,009 images from one South African facility.
- It does not establish performance across different plants, climates, cameras, sensor configurations or annotation protocols.
- It does not show that a particular UAV, GPU, inspection platform or commercial product is required to implement the software pipeline.
For a new project, reproduce the preprocessing and architecture first, then tune only one factor at a time on a leakage-safe validation set. Compare alternatives under the same protocol, preserve the held-out test set, and treat cross-site validation as a requirement when predictions will influence real maintenance decisions.
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