File size: 2,395 Bytes
b261a90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # Roadwork Cones Dataset
This dataset is designed for detecting roadwork-zone objects in autonomous driving scenarios. It contains three classes - traffic cones, roadworks signs, and vertical guide panels (delineators) - captured from four vehicle-mounted cameras across 39 driving sessions in urban and suburban roads.
## Dataset Description
- **Source:** Fleet of autonomous vehicles, urban and suburban roads
- **Sensor:** 4x LUCID TRI054S-CC cameras (2880×1860), front/side facing
- **Coverage:** ~31K bounding box annotations, ~4.7K unique images, 3 classes
- **Splits:** Train 70% (22,841 rows, 2,871 images), Test 30% (8,561 rows, 1,803 images) - split by driving session to prevent temporal leakage
- **Format:** Parquet (annotations) + JPEG (images) in `train/` and `test/` subdirectories
### Classes (3)
| Class | Count | Description |
|-------|-------|-------------|
| cone | 11,520 | Standard traffic cone |
| roadworks | 2,813 | Roadwork zone sign / panel |
| vertical_pannel | 17,069 | Vertical guide panel (delineator) |
### Data Fields
| Field | Type | Description |
|-------|------|-------------|
| bbox_msg_id | VARCHAR | UUID linking to the original bounding box message |
| object_id | BIGINT | Unique object tracking ID |
| label | VARCHAR | Object class (`cone`, `roadworks`, `vertical_pannel`) |
| bbox_coords | DOUBLE[4] | Bounding box [x, y, width, height] in pixel coordinates |
| timestamp_ns | BIGINT | ROS bag timestamp (nanoseconds) |
| image_path | VARCHAR | Relative path to JPEG in `train/` or `test/` |
### Data Splits
| Split | Rows | Images |
|-------|------|--------|
| train | 22,841 | 2,871 |
| test | 8,561 | 1,803 |
### Dataset Structure
```
roadwork_cones_dataset/
├── annotations.parquet # All annotations (31,402 rows)
├── train/
│ ├── camera_1C0FAF5250E2/ # 854 images
│ ├── camera_1C0FAF57D6F8/ # 1,415 images
│ ├── camera_1C0FAF5CA7B6/ # 494 images
│ └── camera_1C0FAF5CC14D/ # 108 images
└── test/
├── camera_1C0FAF5250E2/ # 570 images
├── camera_1C0FAF57D6F8/ # 1,193 images
├── camera_1C0FAF5CA7B6/ # 40 images
└── camera_1C0FAF5CC14D/ # 0 images
```
### Usage
```python
import pandas as pd
df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
```
|