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

✅ TTA-Sim2Real Dataset 

This folder contains a sample version of the TTA-Sim2Real dataset , introduced in our paper "TTA-Sim2Real: A Mixed Real–Synthetic Dataset and Pipeline for Tidal Turbine Assembly Object Detection".
The dataset is designed to support object detection in industrial assembly environments, combining real-world footage , controlled captures , and synthetic renderings . This version includes a representative subset for reproducibility and testing purposes.
TTA-Sim2Real 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-Sim2Real is a multi-source object detection dataset specifically designed for sim-to-real transfer in industrial assembly tasks. It contains over 21,000 annotated images across three data types:

-Spontaneous Real Data : Captured from live assembly and disassembly operations, including operator presence with face blurring for privacy.
-Controlled Real Data : Structured scenes recorded under uniform lighting and positioning using a cobot-mounted high-resolution camera.
-Synthetic Data : 6,000 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/

├ data_access/  

│     ├── spontaneous_real_data/     # Unscripted real-world footage ()

│     ├── controlled_real_data/      # Structured scenes from cobot-mounted camera

│     └── synthetic_data/            # Auto-labeled Unity-generated images with domain randomization

├ data_annotation/          # Annotation files and documentation

│     ├── spontaneous_real_data.zip/     # Manual annotations (where available)

│     ├── 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
1. data_access/
Contains sample subsets from three types of data used in our experiments:

🔹 spontaneous_real_data/
Real-world video captured during live assembly operations.

Useful for sim-to-real evaluation and robustness testing.

🔹 controlled_real_data/

Videos captured using a cobot-mounted high-resolution camera.

Contains structured views of turbine components under uniform lighting and angles.

Includes:

Objects of interest only

Objects with small parts

Close-up shots

🔹 synthetic_data/

6,000 auto-labeled images generated using Unity 2022 and Perception Package.

Domain-randomized backgrounds, lighting, and textures.

Includes bounding boxes and segmentation masks in JSON format.

2. 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/

Fully 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.

Includes JSON files with object positions and segmentation labels.