File size: 3,567 Bytes
6de1c38 | 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 65 66 67 | # Russian Road Signs Dataset
This dataset is designed for training and evaluating traffic sign detection models for autonomous driving. It covers 61 traffic sign classes following the Russian GOST R 52290-2004 standard - warning signs (1.x), priority signs (2.x), prohibitory signs (3.x), mandatory signs (4.x), information signs (5.x), service signs (6.x), and supplementary plates (8.x) - recorded from four vehicle-mounted cameras across 50 driving sessions in urban and suburban environments.
## Dataset Description
- **Source:** Fleet of autonomous vehicles, Russian urban and suburban roads
- **Sensor:** 4x LUCID TRI054S-CC cameras (2880×1860), front/side facing
- **Coverage:** ~54K bounding box annotations, ~6.4K unique images, 61 sign classes
- **Splits:** Train 70% (39,717 rows, 5,031 images), Test 30% (14,564 rows, 1,409 images) - split by driving session to prevent temporal leakage
- **Format:** Parquet (annotations) + JPEG (images) in `train/` and `test/` subdirectories
### Classes (61)
Signs classified according to the Russian GOST R 52290-2004 standard, including:
- **Speed limits and their end signs** — `3_24`, `3_24_s40_white`, `3_24_s50`, `3_24_s60`, `3_24_s70`, `3_24_s70_white`, `3_24_s80`, `3_24_s90`, `3_24_s90_white`, `3_24_s100`, `3_24_s110`, `3_24_s130`, `3_25`, `3_25_white`, `3_25_s30_white`, `3_25_s50`, `3_25_s50_white`, `3_25_s60`, `3_25_s60_white`, `3_25_s70`, `3_24_s10_white`, `3_24_s40`
- **Priority signs** — `2_4` (give way), `3_31` (end of all restrictions)
- **Mandatory direction signs** — `4_1_1` (go straight), `4_2_1`, `4_2_2`, `4_2_3` (obstacle avoidance)
- **Warning signs** — `1_8` (traffic lights), `1_15` (slippery road), `1_16` (rough road), `1_18` (gravel ejection), `1_19` (dangerous shoulder), `1_20_1`, `1_20_2`, `1_20_3` (pedestrian crossing ahead), `1_21` (children), `1_33` (other hazards)
- **Information signs** — `5_1` (motorway), `5_2` (end of motorway), `5_15_2`, `5_15_4`, `5_15_5`, `5_15_6`, `5_15_7` (lane direction)
- **Supplementary plates** — `8_2_3` (action zone end), `8_22_1`, `8_22_2`, `8_22_3` (obstacle type)
- **Miscellaneous** — `table` (speed bump sign), `scoreboard`, `scoreboard_sign` (overhead gantry), `unknown_sign`
### 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 | Traffic sign class label (GOST R 52290-2004) |
| 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 | 39,717 | 5,031 |
| test | 14,564 | 1,409 |
### Dataset Structure
```
road_signs_dataset/
├── annotations.parquet # All annotations (54,281 rows)
├── train/
│ ├── camera_1C0FAF5250E2/ # 3,487 images
│ ├── camera_1C0FAF57D6F8/ # 979 images
│ ├── camera_1C0FAF5CA7B6/ # 464 images
│ └── camera_1C0FAF5CC14D/ # 101 images
└── test/
├── camera_1C0FAF5250E2/ # 793 images
├── camera_1C0FAF57D6F8/ # 149 images
├── camera_1C0FAF5CA7B6/ # 364 images
└── camera_1C0FAF5CC14D/ # 103 images
```
### Usage
```python
import pandas as pd
df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")
``` |