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GenAI relies on a strong data infrastructure to drive its capabilities. With vast datasets and precise management, this backbone enables the AI to learn, adapt, and deliver advanced solutions across industries

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AI-Powered Data Annotation for Health and Life Sciences Research

Client Challenge

A leading autonomous vehicle company needed high-quality labeled 3D LiDAR point cloud data to train their AI models for real-time object detection and navigation. The challenge was to accurately label millions of 3D data points captured by LiDAR sensors to distinguish between cars, pedestrians, cyclists, and road obstacles in various driving conditions.

Objectways Solution

Objectways delivered a comprehensive data annotation solution specifically designed to meet the unique requirements of autonomous vehicle development:

  • Expert 3D Point Cloud Annotation:- Our team of skilled annotators utilized advanced LiDAR labeling tools to tag objects in 3D space with high precision, including bounding boxes and semantic segmentation of complex environments.
  • Human-in-the-Loop Workflow:- By integrating AI-assisted labeling with human review, we ensured the highest levels of accuracy while maintaining efficiency, especially in challenging scenarios like night driving or poor weather conditions.
  • Scalable Data Processing:- We processed large volumes of LiDAR data, delivering high-quality labeled datasets within tight timelines, enabling the client to train their machine learning models faster.
  • Customized Annotations:- Working closely with the client, we developed a tailored annotation schema to meet their specific requirements, including differentiating between moving and stationary objects, object classification, and distance measurements.

Business Value

With Objectways’ support, the client successfully trained their AI models to detect and classify objects in real-time, significantly improving the safety and reliability of their autonomous vehicle systems. Our scalable solution allowed them to accelerate their development cycle and confidently progress toward deployment.

Why Choose Objectways for LiDAR Data Annotation?

  • Expertise in 3D Point Cloud Labeling: Extensive experience in annotating complex 3D LiDAR datasets across diverse industries, including autonomous vehicles, drones, and robotics.
  • High Precision and Quality: Combining AI technology with human oversight to deliver accurate, high-quality annotations.
  • Scalability and Flexibility: Our services are designed to scale with your project, from early-stage prototypes to full-scale production environments.

Object Detection and Classification

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.

Lane Detection and Road Markings

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.

3D Point Cloud Labeling

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.

Sensor Fusion

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.

Traffic Sign Recognition

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.

Pedestrian and Cyclist Behavior Analysis

Use Case

Accurately identify and label various objects in real-world environments, including vehicles, pedestrians, cyclists, traffic signs, and road infrastructure.

Purpose

Train your AI models to recognize and classify objects in real-time, enabling autonomous vehicles to navigate safely and avoid potential hazards.