High-quality dataset curation is critical for improving model performance. This data is sourced through raw sensor mining, extracting relevant snippets based on specific model attributes, or synthetic data from fleet dashcams and ADAS sensors.

With a skilled workforce and deep domain expertise, DDD ensures efficient dataset creation across static images, videos, and multi-sensor outputs.

ML data sanitation is essential to maintaining high-quality inputs for model training. This involves data cleanup, transformation, and integrity testing to eliminate flawed sensor outputs and ensure consistency.

DDD specializes in these processes, leveraging advanced tools and techniques to deliver the most reliable data for training high-performance ML models.

Scenario hunting, or data mining, identifies key interactions, behaviors, and edge cases from vast datasets.

Using manual or semi-automated processes, combined with ML foundation models, DDD efficiently extracts relevant data points and validates automated mining outputs through human-in-the-loop feedback, ensuring precision and reliability.

Multi-sensor fusion labeling plays a crucial role in creating high-fidelity ML datasets, especially for L3-L5 autonomous systems. 

By integrating data from cameras, LiDAR, radar, and sonar, DDD facilitates a holistic perception of complex environments. Our team delivers top-tier 2D and 3D labeling for both full and partial scenes, setting industry benchmarks for the highest accuracy.

Object tracking enables autonomous systems to follow the movement and trajectory of objects in video sequences which is critical in applications ranging from self-driving technology and robotics to mobility applications. 

DDD’s expert annotators ensure precise object tracking across the autonomy industry, enhancing dataset quality for real-world applications.

Semantic segmentation is a fundamental computer vision task that assigns pixel-level labels to images, allowing AI models to deeply understand their environment. 

DDD has extensive experience in segmentation across industries, including autonomous vehicles, geospatial analysis, and robotics, ensuring detailed and high-quality annotations.

Pre-labeling and automation validation streamline the annotation process by leveraging AI-assisted labeling and automated verification.  

Our team works closely with clients to refine pre-label workflows and validate automation, reducing annotation costs while maintaining high-quality standards.