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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.
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
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.
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