The enhanced images improved training viability, allowing synthetic data to support real-world detection. Small or distant objects were left unchanged to preserve accuracy.
Meet our client
Client:
Industry:
Market:
Technology:
Client’s Challenge
The client set out to build an obstacle detection system for crop fields using synthetic 3D-rendered data. However, the low photorealism made it unsuitable for real-world training. The key challenge was to enhance realism without altering critical details like crop rows, human features, or pixel-level accuracy needed for segmentation.
Our Solution
We developed a GenAI enhancement pipeline using diffusion models to convert synthetic images into more photorealistic versions. To preserve semantic consistency, we introduced conditioning controls based on depth maps, edge information (e.g., Canny filter), semantic segmentation masks, and human pose skeletons.
Client’s Benefits
The enhanced images improved training viability, allowing synthetic data to support real-world detection. Small or distant objects were left unchanged to preserve accuracy. The pipeline was a key step in bridging synthetic and real data for agriculture.
