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3145c19 | 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 | # Pillars Dataset
This dataset is designed for detecting roadside vertical structures - pillars, poles, and pylons - which are critical static obstacles for autonomous vehicle navigation. It contains 12K bounding box annotations from two side/rear-facing cameras across 6 driving sessions in urban and suburban environments.
## Dataset Description
- **Source:** Fleet of autonomous vehicles, urban and suburban roads
- **Sensor:** 2x LUCID TRI054S-CC cameras (2880×1860), front/side facing
- **Coverage:** ~12K bounding box annotations, ~1.4K unique images, single class
- **Splits:** Train 70% (11,080 rows, 1,242 images), Test 30% (1,035 rows, 139 images) - split by driving session to prevent temporal leakage
- **Format:** Parquet (annotations) + JPEG (images) in `train/` and `test/` subdirectories
### Classes (1)
| Class | Count | Description |
|-------|-------|-------------|
| pillar | 12,115 | Roadside pillar / pole / pylon |
### 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 (`pillar`) |
| 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 | 11,080 | 1,242 |
| test | 1,035 | 139 |
### Dataset Structure
```
pillars_dataset/
├── annotations.parquet # All annotations (12,115 rows)
├── train/
│ ├── camera_1C0FAF5250E2/ # 41 images
│ └── camera_1C0FAF57D6F8/ # 1,201 images
└── test/
├── camera_1C0FAF5250E2/ # 40 images
└── camera_1C0FAF57D6F8/ # 99 images
```
### Usage
```python
import pandas as pd
df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
```
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