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| license: cc-by-4.0 |
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| ## TTA Dataset: Tidal Turbine Assembly Dataset |
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| 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. |
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| ## Dataset Card Abstract |
| TTA contains over 120,000 annotated images across three data types: |
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| -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. |
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| The dataset targets seven object classes representing key turbine components: |
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| -Tidal-turbine |
| -Body-assembled |
| -Body-not-assembled |
| -Hub-assembled |
| -Hub-not-assembled |
| -Rear-cap-assembled |
| -Rear-cap-not-assembled |
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| ## Folder Structure Overview |
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| dataset/ |
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| 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 |
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| │ ├── spontaneous_real_data.zip/ # Bounding box labels in YOLO format |
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| │ ├── controlled_real_data.zip/ # Bounding box labels in YOLO format |
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| │ ├── synthetic_data.zip/ # Auto-generated JSON and mask labels |
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| └ README.md # This file |
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| ## Dataset Description |
| data_annotation/ |
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| Contains annotation files for training and evaluation: |
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| 🔹 spontaneous_real_data.zip/ |
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| Semi-automatic annotations where available. |
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| Format:YOLO-compatible .txt files. |
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| 🔹 controlled_real_data.zip/ |
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| Annotated with YOLO-style bounding boxes. |
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| High-quality labels created semi-automatically using CVAT with AI-assisted tools. |
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| 🔹 synthetic_data.zip/ |
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| Auto-labeled by Unity with accurate bounding boxes and semantic masks. |
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