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

import pandas as pd

df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")