---
annotations_creators:
- manual
language_creators:
- none
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Lpipe Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- underwater
- semantic-segmentation
- pipelines
- robotics
- marine
- oil-and-gas
- computer-vision
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
---

# Lpipe Dataset

[📄 **DualMatch: A Dual EMA Teacher for Underwater Semi-Supervised Pipeline Segmentation**](http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2025/09.15.00.38/doc/LawsonSIBGRAPI_2025-1.pdf)  

The **Lpipe dataset** is an underwater image dataset designed for **semantic segmentation** tasks involving subsea environments.  
It includes manually annotated RGB images containing **pipelines, humans, animals, and robots**.  
The dataset aims to support research in **underwater computer vision**, **autonomous robotics**, and **oil and gas inspection systems**.

---

## Dataset Summary

The Lpipe dataset provides **716 underwater images** paired with **716 pixel-level segmentation masks**.  
Each mask is color-coded in RGB, where each color corresponds to one of the four semantic classes:  

| Class ID | Class Name | Description |
|-----------|-------------|--------------|
| 0 | Background | Underwater background and seabed |
| 1 | Pipeline | Subsea pipeline or tubular structures |
| 2 | Human | Diver or human presence |
| 3 | Animal | Marine animals (fish, crustaceans, etc.) |
| 4 | Robot | Underwater inspection or maintenance robot |

This dataset was created to assist in the development and evaluation of **semantic segmentation** models for complex underwater scenarios.

---

## Supported Tasks and Benchmarks

The dataset can be used for:
- **Semantic Segmentation**  
- **Semi-supervised Segmentation** (splits available on GitHub)  
- **Object detection and domain adaptation** in underwater imagery  

---

## Dataset Structure

Inside the dataset, two folders are provided:

Lpipe/

- images/ # RGB underwater images (.jpg)
- masks/ # Segmentation masks (.png, RGB-coded)


Although the dataset on Hugging Face includes only images and masks,  
**official training/validation/test splits** (for semi-supervised learning) are available in the associated GitHub repository:  
🔗 [https://github.com/EduardoLawson1/DualMatch](https://github.com/EduardoLawson1/DualMatch)

---

## Data Fields

| Field | Type | Description |
|--------|------|-------------|
| image | `RGB Image (.jpg)` | The original underwater image |
| mask | `RGB Image (.png)` | Pixel-level color-coded segmentation mask |

---

## Data Splits

There are 716 total samples.  
The dataset is not pre-split in the Hugging Face version, but the following splits are defined in the GitHub repository:
- `train.txt`
- `val.txt`
- `test.txt`
- `unlabeled.txt` (for semi-supervised training)

---

## Licenses

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.  
You are free to share, copy, redistribute, adapt, and build upon the material for any purpose, provided that you give proper credit to the original author.  

---

## Citation

If you use this dataset, please cite the related publication:

bibtex
@article{silvadualmatch,
  title={DualMatch: A Dual EMA Teacher for Underwater Semi-Supervised Pipeline Segmentation},
  author={Silva, Eduardo L and Schein, Tatiana T and Briao, Stephanie L and Anastacio, Gabriel L and Oliveira, Felipe G and Drews-Jr, Paulo LJ}
}