---
annotations_creators:
- expert-generated
language: en
license: cc-by-nc-3.0
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- object-detection
task_ids: []
pretty_name: InsPLAD
tags:
- anomaly-detection
- fiftyone
- image
- power-line-inspection
- uav
description: InsPLAD is a Power Line Asset Inspection Dataset and Benchmark containing
  10,561 high-resolution UAV images of 17 unique power line asset categories (28,959
  box annotations), plus cropped-asset subsets for supervised fault classification
  (5 asset types) and unsupervised anomaly detection (17 asset types). This FiftyOne
  build merges all three official sub-datasets into one dataset with a `task` field
  and saved views per sub-task.
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 49706 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("harpreetsahota/InsPLAD")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for InsPLAD

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 49706 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/InsPLAD")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Details

### Dataset Description

InsPLAD (Inspection of Power Line Assets Dataset) is a Power Line Asset
Inspection Dataset and Benchmark built from 10,561 high-resolution UAV
images of real, operating power lines. It covers the three tasks of a
typical power line visual inspection pipeline: (1) object detection of 17
unique power line asset categories in full UAV frames (InsPLAD-det), (2)
supervised classification of visible defects on cropped asset images for 5
asset categories, and (3) unsupervised anomaly detection (normal vs.
anomalous) on cropped asset images for all 17 asset categories. This
FiftyOne build merges all three official sub-datasets into a single
dataset, distinguished by a `task` field and per-task saved views.

- **Curated by:** André Luiz Vieira-e-Silva, Heitor Felix, Francisco
  Simões, Veronica Teichrieb (Universidade Federal de Pernambuco / Centro
  de Informática; Universidade Federal Rural de Pernambuco)
- **Funded by:** Coordenação de Aperfeiçoamento de Pessoal de Nível
  Superior (CAPES), Brazil; National Council for Scientific and
  Technological Development (CNPq), Brazil
- **Shared by:** The original authors, via GitHub and Mendeley Data
- **Language(s):** en (asset/defect class names; not an NLP dataset)
- **License:** cc-by-nc-3.0

### Dataset Sources

- **Repository:** https://github.com/andreluizbvs/InsPLAD
- **Paper:** InsPLAD: A Dataset and Benchmark for Power Line Asset
  Inspection in UAV Images, International Journal of Remote Sensing (2023),
  https://arxiv.org/abs/2311.01619. A companion paper using the same data,
  Attention Modules Improve Image-Level Anomaly Detection for Industrial
  Inspection: A DifferNet Case Study, WACV 2024,
  https://arxiv.org/abs/2311.02747
- **Original data download:** https://data.mendeley.com/datasets/5n3fjgvfyz/1

## Uses

### Direct Use

Training and benchmarking object detectors on power line hardware
categories; training and benchmarking supervised defect classifiers on
cropped asset images; training and benchmarking unsupervised anomaly
detection methods on cropped asset images; research into rare-class data
curation and annotation-efficient workflows (the class distribution is
heavily long-tailed, from 3,169 images down to 56 for the rarest raw
category).

### Out-of-Scope Use

Any commercial use. The source license (CC BY-NC 3.0) is non-commercial
only. The dataset is not intended or suitable for identifying individuals;
it contains no personal or sensitive data by design (aerial images of
power line hardware only). Asset appearance reflects a specific utility
company's hardware standards in Brazil and may not generalize to power
line hardware from other manufacturers, regions, or voltage classes
without further validation.

## Dataset Structure

This is a flat image dataset (`media_type = "image"`), not grouped or
video, with **49,706 samples** total, combining InsPLAD's three official
sub-datasets into one FiftyOne dataset via a `task` field. Three saved
views are provided, one per sub-task: `InsPLAD-det` (10,561 samples),
`InsPLAD-fault-supervised` (11,525 samples), `InsPLAD-anomaly` (27,620
samples).

### Fields

| Field | FiftyOne type | Description |
|-------|---------------|-------------|
| `task` | `StringField` | Which sub-dataset this sample belongs to: `detection`, `fault_classification`, or `anomaly_detection` |
| `ground_truth` | `Detections` | Bounding boxes over 18 raw power line asset categories (InsPLAD-det COCO annotations, converted to relative `[x, y, w, h]`); populated only for `task == "detection"` |
| `asset` | `StringField` | Power line asset category of the cropped image (5 distinct values for `fault_classification`, 17 for `anomaly_detection`); not populated for `detection` samples |
| `fault` | `Classification` | Supervised fault-classification label: `good`, `rust`, `missing_cap`, or `bird_nest`; populated only for `task == "fault_classification"` |
| `anomaly` | `Classification` | Binary label: `normal` or `anomaly`; populated only for `task == "anomaly_detection"` |
| `defect` | `StringField` | Specific defect subtype for anomaly-detection samples: `none`, `rust`, `missing_cap`, `bird_nest`, `torn`, or `peeling_paint`; populated only for `task == "anomaly_detection"` |
| `tags` | list of `str` | Encodes both the sub-dataset (`det`, `fault`, or `anomaly`) and the original split (`train`, `val`, or `test`) |

### Label types and why

- `ground_truth` uses `Detections` because InsPLAD-det's source annotations
  are 2D bounding boxes (no segmentation masks, despite the COCO schema
  having a `segmentation` field; it is always empty in the source data).
- `fault` and `anomaly` use `Classification` (not `Classifications`)
  because each cropped image in these sub-datasets carries exactly one
  label, encoded by its containing folder in the source data.
- `defect` is stored as a plain string field, separate from the `anomaly`
  Classification, so the specific defect subtype can be filtered/faceted
  independently of the binary normal/anomaly label.

### `dataset.info`

`dataset.info` records `source` (the GitHub repository URL), `paper` (the
arXiv URL), and `license` (`CC BY-NC 3.0`).

### Parsing decisions

- **Duplicate COCO entries merged.** InsPLAD-det's `instances_train.json`
  contains 46 `file_name`s that appear under two different `image_id`s,
  each holding a disjoint subset of that image's boxes. These were merged
  into one sample per file with the union of both `image_id`s' detections.
  As a result, the true unique image count for InsPLAD-det is **10,561**,
  not the 10,607 implied by the paper's headline image count.
- **Bounding box conversion.** Source boxes are absolute-pixel COCO
  `[x, y, w, h]`; converted to FiftyOne's relative `[0, 1]` `[x, y, w, h]`
  by dividing by each image's width/height.
- **`sphere` category kept as-is.** InsPLAD-det's COCO `categories` list
  includes an 18th category, `sphere` (26 boxes, train split only), which
  is not one of the paper's 17 named asset categories or its per-class
  benchmark table. It is retained in `ground_truth` rather than dropped,
  since the source data includes it.
- **Fault-classification label vocabulary normalized.** In the source
  data, `supervised_fault_classification`'s `polymer-insulator-upper-shackle`
  asset used Portuguese folder names in its `train` split (`corrosão`,
  `normal`) but English folder names in `val` (`rust`, `good`) for the
  same two conditions. Both were mapped to `rust`/`good` for consistency
  with the other four fault-classification asset categories.
- **Minor spelling normalized across sub-datasets.** The same defect
  concept was spelled differently between the supervised and unsupervised
  sub-datasets in the source data (`missing-cap` vs. `missingcap`;
  `bird-nest` vs. `nest`). Both were normalized to `missing_cap` and
  `bird_nest` respectively across both `fault` and `defect` fields.
- **`defect` uses `"none"` for normal anomaly-detection samples**, rather
  than leaving the field unset, for consistent filtering/faceting.

## Dataset Creation

### Curation Rationale

Automated visual inspection of power line assets is an active research
area, but most power line inspection datasets are proprietary, undisclosed
by the utility companies and agencies that hold them. InsPLAD was created
to provide the first large, real-world, publicly available dataset and
benchmark covering all three stages of a typical inspection pipeline
(asset detection, defect classification, and anomaly detection) on the
same underlying asset categories.

### Source Data

#### Data Collection and Processing

Images were captured by UAV (drone) during real-world inspections of
operating power lines, at 1920×1080 resolution, under varied environmental
conditions, orientations, and distances. `InsPLAD-fault`'s two sub-datasets
(`supervised_fault_classification` and `unsupervised_anomaly_detection`)
were derived from InsPLAD-det: annotated objects for five (supervised) and
seventeen (anomaly detection) asset categories were cropped to a
near-square proportion and labeled at the image level according to visible
condition (normal, or the specific defect present). For supervised fault
classification, faulty training samples were augmented roughly 10x using
the Albumentations library to balance otherwise rare defect classes; for
unsupervised anomaly detection, defective examples were deliberately
withheld from training and included only in the test split, consistent
with the unsupervised anomaly detection setting.

#### Who are the source data producers?

UAV inspection imagery of real, operating power lines, collected by the
Voxar Labs group at Universidade Federal de Pernambuco.

### Annotations

#### Annotation process

Bounding boxes for InsPLAD-det were drawn using the LabelImg tool by two
annotators, instructed by specialists on asset naming conventions and the
regions of interest that should be contained in each box, with annotators
in constant contact to standardize labeling. Fault and anomaly labels were
applied at the image level based on the visible condition of each cropped
asset.

#### Who are the annotators?

Two human annotators, instructed and guided by domain specialists, per the
source paper.

#### Personal and Sensitive Information

Not addressed explicitly in the source paper. Images are aerial captures
of power line hardware and surrounding infrastructure; there is no
statement in the source material regarding incidental capture of people or
other personal data. `[More Information Needed]`

## Citation

**BibTeX:**

```
@article{doi:10.1080/01431161.2023.2283900,
   author    = {André Luiz Buarque Vieira e Silva, Heitor de Castro Felix, Franscisco Paulo Magalhães Simões, Veronica Teichrieb, Michel dos Santos, Hemir Santiago, Virginia Sgotti and Henrique Lott Neto},
   title     = {InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images},
   journal   = {International Journal of Remote Sensing},
   volume    = {44},
   number    = {23},
   pages     = {1-27},
   year      = {2023},
   publisher = {Taylor & Francis},
   doi       = {10.1080/01431161.2023.2283900},
   URL       = {https://doi.org/10.1080/01431161.2023.2283900},
   eprint    = {https://doi.org/10.1080/01431161.2023.2283900},
}

@InProceedings{Vieira_2024_WACV,
    author    = {e Silva, Andr\'e Luiz Vieira and Sim\~oes, Francisco and Kowerko, Danny and Schlosser, Tobias and Battisti, Felipe and Teichrieb, Veronica},
    title     = {Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {8246-8255}
}
```

**APA:**

Vieira-e-Silva, A. L. B., de Castro Felix, H., Simões, F. P. M., Teichrieb, V., dos Santos, M., Santiago, H., Sgotti, V., & Lott Neto, H. (2023). InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images. *International Journal of Remote Sensing*, 44(23), 1-27.

## More Information

This FiftyOne build merges the three official InsPLAD sub-datasets
(`InsPLAD-det.zip`, `supervised_fault_classification.zip`,
`unsupervised_anomaly_detection.zip`) into one dataset. See the source
repository (https://github.com/andreluizbvs/InsPLAD) for the original,
separately-distributed sub-datasets and additional documentation.

## Dataset Card Authors

Harpreet Sahota (FiftyOne / Voxel51 conversion and card)

## Dataset Card Contact

harpreetsahota