At ProjectBinder, we are at the forefront of revolutionizing computer vision workflows in manufacturing environments. Our innovative approach leverages Synthetic Data to overcome the challenges of data scarcity, privacy concerns, and high costs, ensuring the success of AI models.
How Synthetic Data Brings Value
Synthetic data can be effortlessly generated in multiple modalities, including RGB format images, depth images, segmented images, instance segmented images, and 2D and 3D bounding boxes.
This versatility allows us to address various computer vision tasks, such as classification, object detection, and segmentation.
Stages Where Synthetic Data Adds Value
1. Proof-of-Concept ML Solutions: Testing novel ML algorithms without waiting for accurate data.
2. Scalability: Enhancing real-world data with additional synthetic data to build trust and improve model performance.
3. Simulation Models: Developing metamodels to approximate simulation models, reducing computational costs.
Synthetic data in different scenarios
Synthetic data offers tremendous value in various scenarios, including:
- Detection and tracking of people in different environments.
- Defect detection.
- 6D object poses estimation for line clearance.
- Layout optimization in manufacturing and warehouse operations.
Benefits
- Model Performance: Synthetic data and real-world data produce comparable results, with both models scoring 87% accuracy.
- Time Savings: Customers need not wait for a production setting to generate training data, allowing for faster model development and deployment.
Simulating people and machines in a process enables customers to identify necessary changes during the design phase, ensuring optimal performance before actual implementation.