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| license: cc-by-4.0 |
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| ✅ TTA-Sim2Real Dataset |
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| 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. |
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| ✅ 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: |
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| -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. |
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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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| ├ data_access/ |
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| │ ├── spontaneous_real_data/ # Unscripted real-world footage () |
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| │ ├── controlled_real_data/ # Structured scenes from cobot-mounted camera |
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| │ └── synthetic_data/ # Auto-labeled Unity-generated images with domain randomization |
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| ├ data_annotation/ # Annotation files and documentation |
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| │ ├── spontaneous_real_data.zip/ # Manual annotations (where available) |
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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 |
| 1. data_access/ |
| Contains sample subsets from three types of data used in our experiments: |
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| 🔹 spontaneous_real_data/ |
| Real-world video captured during live assembly operations. |
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| Useful for sim-to-real evaluation and robustness testing. |
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| 🔹 controlled_real_data/ |
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| Videos captured using a cobot-mounted high-resolution camera. |
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| Contains structured views of turbine components under uniform lighting and angles. |
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| Includes: |
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| Objects of interest only |
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| Objects with small parts |
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| Close-up shots |
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| 🔹 synthetic_data/ |
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| 6,000 auto-labeled images generated using Unity 2022 and Perception Package. |
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| Domain-randomized backgrounds, lighting, and textures. |
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| Includes bounding boxes and segmentation masks in JSON format. |
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| 2. 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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| Fully 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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| Includes JSON files with object positions and segmentation labels. |
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