| # Pillars Dataset |
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| 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. |
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| ## Dataset Description |
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|
| - **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 |
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|
| ### Classes (1) |
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| | Class | Count | Description | |
| |-------|-------|-------------| |
| | pillar | 12,115 | Roadside pillar / pole / pylon | |
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| ### Data Fields |
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| | 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/` | |
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| ### Data Splits |
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| | Split | Rows | Images | |
| |-------|------|--------| |
| | train | 11,080 | 1,242 | |
| | test | 1,035 | 139 | |
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| ### Dataset Structure |
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|
| ``` |
| 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 |
| ``` |
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| ### Usage |
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| ```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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