Accelerating AI-driven Object Detection to Enhance Digital Twin Solutions for a Smart Infrastructure Leader

“Inconsistent asset identification and quality control issues can lead to errors in digital twin representations, impacting engineering planning and decision making.”

Background and Business Challenges

When Enhancing Data Accuracy is
Crucial for Digital Twin Development

LiDAR-based scans serve as a foundational data source for digital twin solutions, providing high-resolution spatial information essential for asset modeling. However, ensuring data accuracy requires precise tagging and validation across multiple sources. Inconsistent asset identification and quality control issues can lead to errors in digital twin representations, impacting engineering planning and decision making.

Solution and Benefits

AI-powered Object Detection
for LiDAR Data

Infosys developed a proof of concept to enhance LiDAR data quality by labeling mechanical component geometries and testing customer-specific object detection AI models. This improved the accuracy of asset identification and validation, ensuring more reliable Digital Twin workflows.

Benefits:

Enhanced Data Accuracy: AI-driven tagging ensures precise mapping of assets in digital twin environments.
Optimized Engineering Planning: Reliable digital twin models improve decision making in infrastructure and asset management.
Increased Productivity: Automated object detection reduces manual effort and accelerates quality control processes.