---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
pretty_name: DADA-seg
tags:
- multimodal
- autonomous-driving
- event-camera
- accident-scene
- semantic-segmentation
- iros-2021
- tits-2021
---

# DADA-seg: Multimodal Accident Scene Segmentation Benchmark

[IROS 2021 & IEEE T-ITS 2021] Official Dataset Repository for the **DADA-seg** accident scene segmentation benchmark.

- **GitHub Repository:** https://github.com/InSAI-Lab/ISSAFE
- **Model Checkpoints:** [InSAI-Lab/ISSAFE](https://huggingface.co/InSAI-Lab/ISSAFE)
- **Collection:** [ISSAFE & EDCNet (IROS 2021 & T-ITS 2021)](https://huggingface.co/collections/InSAI-Lab/issafe-and-edcnet-iros-2021-and-t-its-2021-6ad1b2429101a4c06509e4cb)
- **Paper (IROS 2021):** [ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data](https://arxiv.org/abs/2008.08974)
- **Paper (IEEE T-ITS 2021):** [Exploring Event-Driven Dynamic Context for Accident Scene Segmentation](https://arxiv.org/abs/2112.05006)

---

## Dataset Description

Ensuring traffic safety requires robust environmental perception even under severe accident scenarios. Most critical traffic accident scenes involve extreme dynamics, high-speed motions, vehicle rollovers, collisions, and motion blur, causing conventional frame-based RGB segmentation models to degrade drastically.

**DADA-seg** is a pixel-wise annotated multimodal benchmark selected from DADA-2000. It provides paired:
- **RGB image frames** (anchor frames and preceding frames)
- **High-temporal event camera volume representations** (`.npz` files with temporal bins)
- **Fine-grained pixel-level semantic ground truth** annotations

The validation set contains **313 carefully verified and annotated accident scenes** across challenging traffic conditions.

## File Structure

The complete dataset archive `DADA-seg.tar.gz` (~448 MB) unpacks into the following standard directory structure:

```text
DADA_seg/
├── event/
│   └── val/              # Event volume tensors (.npz)
├── gtFine/
│   └── val/              # Pixel-level semantic annotations (19 Cityscapes-compatible classes)
└── leftImg8bit/
    └── val/              # Corresponding RGB image frames
```

In addition to `DADA-seg.tar.gz`, modular archives are also provided:
- `dada_val_img_label.tar.gz` (~47 MB): Contains `leftImg8bit/val` and `gtFine/val`.
- `dada_val_event.tar.gz` (~402 MB): Contains `event/val` volume tensors.
- `dada_val_label_only.zip` (~2.8 MB): Contains only ground truth semantic masks (`gtFine/val`).

## Benchmark Results

Evaluation on DADA-seg demonstrates that incorporating event-driven dynamic context brings **+8.2% mIoU gain** over RGB-only baselines and outperforms >20 competitive segmentation architectures under accident scenarios.

| Method | Modality | Backbone | DADA-seg mIoU (%) | Gain over RGB |
| :--- | :---: | :---: | :---: | :---: |
| ResNet Baseline | RGB | ResNet-18 | 29.8% | - |
| ISSAFE (IROS 2021) | RGB + Event | ResNet-18 | 34.5% | +4.7% |
| **EDCNet (T-ITS 2021)** | **RGB + Event ($B=2$)** | **ResNet-18** | **38.0%** | **+8.2%** |

## Quick Start & Download

Using the Hugging Face CLI:

```bash
# Download complete dataset archive
hf download InSAI-Lab/DADA-seg DADA-seg.tar.gz --type dataset --local-dir ./dataset

# Extract into dataset directory
cd ./dataset
tar -zxvf DADA-seg.tar.gz
```

Using Python (`huggingface_hub`):

```python
from huggingface_hub import hf_hub_download

dataset_path = hf_hub_download(
    repo_id="InSAI-Lab/DADA-seg",
    repo_type="dataset",
    filename="DADA-seg.tar.gz",
    local_dir="./dataset"
)
```

## Citation

```bibtex
@inproceedings{zhang2021issafe,
  author={Zhang, Jiaming and Yang, Kailun and Stiefelhagen, Rainer},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data}, 
  year={2021},
  pages={1132-1139},
  doi={10.1109/IROS51168.2021.9636109}
}

@article{zhang2021edcnet,
  author={Zhang, Jiaming and Yang, Kailun and Stiefelhagen, Rainer},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={Exploring Event-Driven Dynamic Context for Accident Scene Segmentation}, 
  year={2021},
  pages={1-17},
  doi={10.1109/TITS.2021.3134828}
}
```

## License

This dataset is released under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).