Datasets:

EPDCL
/

pd-defect

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

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); BibTeX is given on the main dataset card.

Perspective Validation Module - PD-Defect

TL;DR