TRAFFIC SIGN DATASETS

Develop traffic sign detection and classification efficiently

Applied Intuition’s Traffic Sign Datasets provide diverse, physically accurate, and pixel-perfect labeled images of traffic signs for machine learning (ML) model training. Improve performance on rare classes, confidently develop advanced driver-assistance systems (ADAS), and vastly reduce deployment time and data cost.

CASE STUDY

Improve performance while reducing real-world data requirements by 90%

Applied’s perception team conducted a study to examine how synthetic data helps improve traffic sign classification performance.

Read the case study

Key features

Physically accurate synthetic data generated with validated, hardware-specific sensor models

Error-free ground truth labels in industry standard formats

Proprietary, state-of-the-art techniques to reduce and mitigate the sim-to-real domain gap

Thousands of sign faces and posts with procedurally generated variants

Diverse environments and weather to maximize diversity and ensure model robustness

Global coverage (regions include the U.S., Canada, Europe, Japan, China, South Korea, and more)

INTELLIGENT SPEED ASSISTANCE

Meet new regulatory requirements

Intelligent speed assistance (ISA) is mandated by the European Union (EU) General Safety Regulation (GSR). Applied’s Synthetic Datasets, including our Traffic Sign Datasets, help accelerate the development of ISA systems with accurate traffic sign detection and recognition abilities, reducing reliance on real labeled data.

KEY BENEFITS

Rapid, low-cost training and deployment of models for traffic sign detection and classification

Improve system performance

Leverage synthetic images to address edge cases, target data sparsity issues, and resolve class imbalances

Reduce cost

Train a high-performance ML model with synthetic data to reduce real data collection and labeling

Shorten development cycles

Immediately obtain new training datasets when failures occur in testing or production, rather than waiting for collection and labeling

TRAFFIC SIGN DATASETS

Develop traffic sign detection and classification efficiently

CASE STUDY

Improve performance while reducing real-world data requirements by 90%

Key features

Physically accurate synthetic data generated with validated, hardware-specific sensor models

Error-free ground truth labels in industry standard formats

Proprietary, state-of-the-art techniques to reduce and mitigate the sim-to-real domain gap

Thousands of sign faces and posts with procedurally generated variants

Diverse environments and weather to maximize diversity and ensure model robustness

Global coverage (regions include the U.S., Canada, Europe, Japan, China, South Korea, and more)

INTELLIGENT SPEED ASSISTANCE

Meet new regulatory requirements

KEY BENEFITS

Rapid, low-cost training and deployment of models for traffic sign detection and classification

Improve system performance

Leverage synthetic images to address edge cases, target data sparsity issues, and resolve class imbalances

Reduce cost

Train a high-performance ML model with synthetic data to reduce real data collection and labeling

Shorten development cycles

Immediately obtain new training datasets when failures occur in testing or production, rather than waiting for collection and labeling

SOLUTIONS

Company

TRAFFIC SIGN DATASETS

Develop traffic sign detection and classification efficiently

CASE STUDY

Improve performance while reducing real-world data requirements by 90%

Key features

Physically accurate synthetic data generated with validated, hardware-specific sensor models

Error-free ground truth labels in industry standard formats

Proprietary, state-of-the-art techniques to reduce and mitigate the sim-to-real domain gap

Thousands of sign faces and posts with procedurally generated variants

Diverse environments and weather to maximize diversity and ensure model robustness

Global coverage (regions include the U.S., Canada, Europe, Japan, China, South Korea, and more)

INTELLIGENT SPEED ASSISTANCE

Meet new regulatory requirements

KEY BENEFITS

Rapid, low-cost training and deployment of models for traffic sign detection and classification

Improve system performance

Leverage synthetic images to address edge cases, target data sparsity issues, and resolve class imbalances

Reduce cost

Train a high-performance ML model with synthetic data to reduce real data collection and labeling

Shorten development cycles

Immediately obtain new training datasets when failures occur in testing or production, rather than waiting for collection and labeling

Get started with Traffic Sign Datasets

SOLUTIONS

Company