---
license: cc-by-4.0
---


## TTA Dataset: Tidal Turbine Assembly Dataset

This folder contains a sample version of the TTA (Tidal Turbine Assembly) dataset , introduced in our paper "Computer Vision as a Data Source for Digital Twins in Manufacturing: a Sim2Real Pipeline".
The dataset is designed to support object detection in industrial assembly environments, combining controlled captures, synthetic renderings, and real-world footage desired for test . This version includes a representative subset with annotations for reproducibility and testing purposes.
TTA is a mixed-data object detection dataset designed for sim-to-real research in industrial assembly environments. It includes spontaneous real-world footage , controlled real data captured via cobot-mounted camera , and domain-randomized synthetic images generated using Unity, targeting seven classes related to tidal turbine components at various stages of assembly. The dataset supports reproducibility and benchmarking for vision-based digital twins in manufacturing.



![image/png](https://cdn-uploads.huggingface.co/production/uploads/6824a993a483759e267a5f43/vLIuDGB3H3Gkw-WTSDehp.png)

## Dataset Card Abstract 
TTA  contains over 120,000 annotated images across three data types:

-Spontaneous Real Data : Captured from live assembly and disassembly operations, including operator presence with face blurring for privacy, dedicated for test and fine-tuning.
-Controlled Real Data : 15, 000 Structured scenes recorded under uniform lighting and positioning using a cobot-mounted high-resolution camera.
-Synthetic Data : 105,000 of auto-labeled images generated using Unity 2022 with domain randomization techniques.

The dataset targets seven object classes representing key turbine components:

-Tidal-turbine
-Body-assembled
-Body-not-assembled
-Hub-assembled
-Hub-not-assembled
-Rear-cap-assembled
-Rear-cap-not-assembled


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6824a993a483759e267a5f43/rAPOJSummo6nHgJV5Ra_Y.png)

## Folder Structure Overview

dataset/
        
The full dataset, including video recordings, will be made publicly available upon publication. To ensure reproducibility, the annotations are provided for evaluation purposes.
├ data_annotation/          # Annotation files and documentation

│     ├── spontaneous_real_data.zip/     # Bounding box labels in YOLO format

│     ├── controlled_real_data.zip/      # Bounding box labels in YOLO format

│     ├── synthetic_data.zip/            # Auto-generated JSON and mask labels

└ README.md                 # This file

## Dataset Description
data_annotation/
   
Contains annotation files for training and evaluation:

🔹 spontaneous_real_data.zip/

Semi-automatic annotations where available.

Format:YOLO-compatible .txt files.

🔹 controlled_real_data.zip/

Annotated with YOLO-style bounding boxes.

High-quality labels created semi-automatically using CVAT with AI-assisted tools.

🔹 synthetic_data.zip/

Auto-labeled by Unity with accurate bounding boxes and semantic masks.