Datasets:
Formats:
imagefolder
Size:
10K - 100K
Tags:
image
computer-vision
electrical-infrastructure
defect-detection
smart-grid
power-distribution
License:
| # Perspective Validation Module - PD-Defect | |
| Collection, licensing, and citation are documented on the [main dataset card](../README.md). | |
| ## TL;DR | |
| Binary classification of full images, pole-top crops, and DTR scenes as Valid or Perspective-Distorted. The combined release contains 4,950 samples across three subtasks. The task concerns capture viewpoint only, not lens distortion, image sharpness, object completeness, or defect presence. Borderline cases lie on a continuum and are forced into a binary label. | |
| ## Task Definition | |
| Classify each input as geometrically Valid or Perspective-Distorted for the analysis task that depends on that region (lean assessment, component tilt assessment, or distribution-transformer-to-ground clearance). | |
| Images rejected for perspective distortion in the inspection datasets were retained in this release, including the validation and test splits, and were used to train the DINOv3 perspective-validation models. | |
| ## Label Definitions | |
| - **Valid** (folder `accepted`): the viewpoint preserves the geometric relationship required by the corresponding analysis task. | |
| - **Perspective-Distorted** (folder `rejected_perspective_issue`): the viewpoint compromises that relationship, so structural interpretation from the image would be unreliable. | |
| ## What Perspective Distortion Means in Each Subtask | |
| ### Full-image (Pole Lean Assessment) | |
| The pole must be photographed from a viewpoint facing it directly so that its imaged axis reflects true vertical alignment. Under an oblique angle, an upright pole can appear to lean and a leaning pole can appear upright. | |
| ### Pole-top crops (Crossarm and Top-Cleat Tilt Assessment) | |
| The pole-top region must be viewed close to head-on so that Crossarm and Top Cleat orientation in the image reflects true structure. Foreshortening under an oblique view can reverse Straight and Tilted appearance. A Valid full-image label does not imply a Valid crop: the pole top occupies only a small fraction of a full frame. | |
| ### DTR (plinth–ground clearance) | |
| The pole and plinth must stand at approximately the same distance from the camera so that their relative image heights remain comparable. | |
| ## Subtasks | |
| ### 1) Full-image | |
| - Input: full-frame smartphone images | |
| - Size: 2,169 images (1,018 Valid, 1,151 Perspective-Distorted) | |
| - Split: train 1,605 (74.00%) / val 282 (13.00%) / test 282 (13.00%) | |
| ### 2) Pole-top crops | |
| - Input: pole-top crops from the same crop family as Crossarm and Top-Cleat Tilt Assessment and Voltage Level Classification | |
| - Size: 1,743 images (945 Valid, 798 Perspective-Distorted) | |
| - Split: train 1,221 (70.05%) / val 260 (14.92%) / test 262 (15.03%) | |
| ### 3) DTR (`plinth_dtr/`) | |
| - Input: DTR pole–plinth images | |
| - Size: 1,038 images (438 Valid, 600 Perspective-Distorted) | |
| - Split: train 768 (73.99%) / val 135 (13.01%) / test 135 (13.01%) | |
| ## Data Acquisition and Labeling Protocol | |
| Perspective labels are assigned during curation from geometric interpretability criteria. The Perspective-Distorted class is supplemented by pathway rejects and by other dedicated or repurposed field captures: | |
| - **Full-image:** frames excluded from Pole Lean Assessment object detection for perspective distortion (also labeled `Rejected` in that module's classification subset), plus other dedicated or repurposed captures | |
| - **Pole-top crops:** crops that fail the Crossarm and Top-Cleat Tilt Assessment pole-top perspective screen | |
| - **DTR:** pole–plinth perspective screening rejects | |
| Visibility-only `Rejected` images from Pole Lean Assessment are not placed in perspective validation. | |
| ## Dataset Structure | |
| ```text | |
| perspective_validation/ | |
| ├── full_image/ | |
| │ ├── dataset.json | |
| │ ├── labels.csv | |
| │ ├── train/ | |
| │ │ ├── accepted/ | |
| │ │ ├── rejected_perspective_issue/ | |
| │ │ ├── train.csv | |
| │ │ └── train.json | |
| │ ├── val/ | |
| │ │ ├── accepted/ | |
| │ │ ├── rejected_perspective_issue/ | |
| │ │ ├── val.csv | |
| │ │ └── val.json | |
| │ └── test/ | |
| │ ├── accepted/ | |
| │ ├── rejected_perspective_issue/ | |
| │ ├── test.csv | |
| │ └── test.json | |
| ├── pole_top_crops/ # same layout as full_image/ | |
| └── plinth_dtr/ # same layout as full_image/ | |
| ``` | |
| All three subtasks use `val/` for the validation split. | |
| ## Baseline Benchmarks | |
| DINOv3 ViT-B/16 (`facebook/dinov3-vitb16-pretrain-lvd1689m`), fine-tuned independently on each subtask. Split Accuracy is overall. Class-wise Accuracy, Precision, Recall, F1, and support (`n`) are from the same evaluation. Class-wise Accuracy is `correct / n` and equals Recall. Macro-F1 is the unweighted mean of the class-wise F1 scores. | |
| ### Full-image | |
| `val` **(282)** — Accuracy 89.36% (252/282); Macro-F1 89.33% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 89.47% | 88.15% | 89.47% | 88.81% | 133 | | |
| | rejected_perspective_issue | 89.26% | 90.48% | 89.26% | 89.86% | 149 | | |
| `test` **(282)** — Accuracy 90.43% (255/282); Macro-F1 90.41% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 91.67% | 88.32% | 91.67% | 89.96% | 132 | | |
| | rejected_perspective_issue | 89.33% | 92.41% | 89.33% | 90.85% | 150 | | |
| ### Pole-top crops | |
| `val` **(260)** — Accuracy 95.00% (247/260); Macro-F1 94.95% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 97.16% | 93.84% | 97.16% | 95.47% | 141 | | |
| | rejected_perspective_issue | 92.44% | 96.49% | 92.44% | 94.42% | 119 | | |
| `test` **(262)** — Accuracy 96.56% (253/262); Macro-F1 96.54% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 97.18% | 96.50% | 97.18% | 96.84% | 142 | | |
| | rejected_perspective_issue | 95.83% | 96.64% | 95.83% | 96.23% | 120 | | |
| ### DTR | |
| `val` **(135)** — Accuracy 91.11% (123/135); Macro-F1 90.89% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 89.47% | 89.47% | 89.47% | 89.47% | 57 | | |
| | rejected_perspective_issue | 92.31% | 92.31% | 92.31% | 92.31% | 78 | | |
| `test` **(135)** — Accuracy 85.19% (115/135); Macro-F1 84.89% | |
| | Class | Accuracy | Precision | Recall | F1 | `n` | | |
| | -------------------------- | -------- | --------- | ------ | ------ | --- | | |
| | accepted | 84.21% | 81.36% | 84.21% | 82.76% | 57 | | |
| | rejected_perspective_issue | 85.90% | 88.16% | 85.90% | 87.01% | 78 | | |
| ## Limitations | |
| Binary labels do not encode fine-grained perspective quality, and borderline cases are rejected conservatively. | |
| ## Examples | |
| Random predictions on each subtask's test set. Green **P** / **T** denote prediction and ground truth; red marks an error. | |
|  | |
|  | |
|  | |
| ## Citation | |
| Please cite PD-Defect ([DOI 10.5281/zenodo.18074045](https://doi.org/10.5281/zenodo.18074045)); BibTeX is given on the [main dataset card](../README.md). | |