Datasets:

EPDCL
/

pd-defect

Three subsets are released: object detection (object_detection/; Leaned_Pole / Straight_Pole), image classification (classification/; Leaned / Straight / Rejected), and keypoint detection (keypoint_detection/; bottom / top, kpt_shape [2, 3]). Object detection comprises 1,801 images (2,182 instances); classification, 2,274 images; keypoints, 1,798 images (2,025 pole instances).

Task Definition

Object Detection (object_detection/): each annotated pole is labeled Leaned_Pole or Straight_Pole.
Classification (classification/): each image receives one of Leaned, Straight, or Rejected.
Keypoints (keypoint_detection/): each pole has a bounding box and structural-axis endpoints bottom and top in YOLO pose format (kpt_shape: [2, 3]).

Label Definitions

Leaned_Pole / Leaned: the pole shows a consistent visual deviation from vertical relative to the ground line and other fixed reference structures visible in the image.
Straight_Pole / Straight: the pole shows no observable deviation from vertical.
Rejected (classification only): the primary pole cannot be assessed reliably because the top or base is obscured, the viewpoint is severely distorted, or the alignment is ambiguous.

Annotation Protocol

Object Detection

Boxes enclose the pole shaft from top to base. Crossarms, top cleats, conductors, and background are excluded. Only poles with both top and base clearly identifiable are annotated. This box scope differs from Pole Detection, where overlapping fittings that intersect the shaft are included. Captures that fail those visibility or perspective criteria are excluded from this object-detection subset.

Classification

Whole-image label for the primary pole (Leaned, Straight, Rejected).

Keypoints

Along the pole centerline:

bottom (keypoint 0): base at the ground line
top (keypoint 1): visible tip

YOLO pose order: bottom then top, each (x, y, visibility). When partial occlusion by vegetation or other objects occurred, the centerline was continued through the occluded region only when its geometry could be reliably inferred; otherwise, samples with indeterminate keypoints were excluded.

Perspective

Lean assessment requires an approximately head-on viewpoint so that the imaged pole axis reflects true vertical alignment. Under an oblique angle, judgment can be affected. Such captures are excluded from object detection and labeled Rejected in classification. The same criterion defines the full-image subtask of Perspective Validation.

Class Distribution

Object Detection (instances, all splits): Leaned_Pole 912; Straight_Pole 1,270

Classification (images): Leaned 619 (464 / 93 / 62); Straight 1,281 (962 / 191 / 128); Rejected 374 (280 / 57 / 37)

Keypoints: pole 2,025

Annotation Format

Object Detection — YOLO:

<class_id> <center_x> <center_y> <width> <height>

Class 0 = Leaned_Pole, Class 1 = Straight_Pole

Classification — folder name = class, plus labels.csv (image_id,label)

Keypoints — YOLO pose (kpt_shape: [2, 3]):

<class_id> <center_x> <center_y> <width> <height> <kx1> <ky1> <v1> <kx2> <ky2> <v2>

Class 0 = pole; keypoint 0 = bottom, keypoint 1 = top

Dataset Structure

pole_lean_assessment/
├── object_detection/
│   ├── data.yaml
│   ├── dataset.json
│   ├── train/
│   │   ├── images/
│   │   ├── labels/
│   │   └── train.json
│   ├── valid/
│   │   ├── images/
│   │   ├── labels/
│   │   └── val.json
│   └── test/
│       ├── images/
│       ├── labels/
│       └── test.json
├── classification/
│   ├── dataset.json
│   ├── labels.csv
│   ├── train/
│   │   ├── Leaned/
│   │   ├── Straight/
│   │   ├── Rejected/
│   │   ├── train.csv
│   │   └── train.json
│   ├── val/
│   │   ├── Leaned/
│   │   ├── Straight/
│   │   ├── Rejected/
│   │   ├── val.csv
│   │   └── val.json
│   └── test/
│       ├── Leaned/
│       ├── Straight/
│       ├── Rejected/
│       ├── test.csv
│       └── test.json
└── keypoint_detection/
    ├── data.yaml
    ├── images/
    │   ├── train/
    │   ├── valid/
    │   └── test/
    ├── labels/
    │   ├── train/
    │   ├── valid/
    │   └── test/
    └── JSON/
        ├── dataset.json
        ├── train.json
        ├── val.json
        └── test.json

Object detection and keypoints use valid/; classification uses val/.

Train / Validation / Test Split

Object detection: train 1,441 (80.01%) / valid 181 (10.05%) / test 179 (9.94%); total 1,801
Classification: train 1,706 (75.02%) / val 341 (15.00%) / test 227 (9.98%); total 2,274
Keypoints: train 1,438 (79.98%) / valid 181 (10.07%) / test 179 (9.96%); total 1,798

Baseline Benchmarks

Object Detection — YOLO12m (Leaned_Pole, Straight_Pole). Overall box metrics are given in the split headers; class-wise metrics follow.

valid — Precision 87.31%; Recall 92.41%; mAP@0.5 95.73%; mAP@0.5:0.95 63.84%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
Leaned_Pole	87.26%	92.31%	96.16%	70.76%
Straight_Pole	87.35%	92.50%	95.29%	56.92%

test — Precision 81.93%; Recall 88.56%; mAP@0.5 93.82%; mAP@0.5:0.95 65.90%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
Leaned_Pole	77.71%	83.19%	90.96%	69.89%
Straight_Pole	86.14%	93.94%	96.67%	61.92%

Image-level metrics

Image-level presence/absence per class at detection confidence 0.35 (binary Accuracy, Precision, Recall, and F1 over images).

valid (181 images)

Class	Accuracy	Precision	Recall	F1
Leaned_Pole	93.92%	93.70%	97.54%	95.58%
Straight_Pole	95.58%	93.42%	95.95%	94.67%

test (179 images)

Class	Accuracy	Precision	Recall	F1
Leaned_Pole	89.94%	88.51%	90.59%	89.53%
Straight_Pole	91.06%	90.15%	97.54%	93.70%

Keypoints — YOLO12m pose (pole). Box metrics use detection IoU; pose metrics use Object Keypoint Similarity (OKS).

Box — valid — Precision 94.12%; Recall 91.51%; mAP@0.5 96.66%; mAP@0.5:0.95 68.73%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
pole	94.12%	91.51%	96.66%	68.73%

Pose (OKS) — valid — Precision 96.47%; Recall 90.33%; mAP@0.5 98.19%; mAP@0.5:0.95 96.27%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
pole	96.47%	90.33%	98.19%	96.27%

Box — test — Precision 95.81%; Recall 92.40%; mAP@0.5 96.58%; mAP@0.5:0.95 69.43%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
pole	95.81%	92.40%	96.58%	69.43%

Pose (OKS) — test — Precision 96.86%; Recall 93.40%; mAP@0.5 97.75%; mAP@0.5:0.95 96.85%

Class	Precision	Recall	`mAP@0.5`	`mAP@0.5:0.95`
pole	96.86%	93.40%	97.75%	96.85%

Classification — DINOv3 ViT-B/16 (facebook/dinov3-vitb16-pretrain-lvd1689m). Split Accuracy is overall. Class-wise Precision, Recall, F1, and support (n) are from the same evaluation. Macro-F1 is the unweighted mean of the three class-wise F1 scores.

val (341) — Accuracy 83.58% (285/341); Macro-F1 80.57%

Class	Precision	Recall	F1	`n`
Leaned	84.15%	74.19%	78.86%	93
Rejected	82.98%	68.42%	75.00%	57
Straight	83.49%	92.67%	87.84%	191

test (227) — Accuracy 84.14% (191/227); Macro-F1 81.74%

Class	Precision	Recall	F1	`n`
Leaned	81.82%	72.58%	76.92%	62
Rejected	75.61%	83.78%	79.49%	37
Straight	87.79%	89.84%	88.80%	128

Limitations

Limitations specific to this module:

Labels are binary (Leaned / Straight) and do not include lean magnitude, direction, or measured angle.
Lean was judged from imagery only; no inclinometer or survey measurements accompany the labels.
Poles with very slight deviation sit near the Leaned/Straight boundary and may be labeled inconsistently despite protocol and review.
Lean is assessed in the image plane. A pole leaning directly toward or away from the camera may present little apparent deviation and be labeled Straight.

Examples

Object detection samples from the test set. Boxes show predicted class and confidence.

Keypoint samples. The line joins the pole-axis endpoints from bottom to top.

Classification samples from the test set. Green P / T denote prediction and ground truth; red marks an error.

Citation

Please cite PD-Defect (DOI 10.6084/m9.figshare.34329402); BibTeX is given on the main dataset card.