synthetic data for ai training
Train your ai without
hunting for defects.
Building an AI inspection system means collecting thousands of training images — including defects that might appear just once every few thousand parts. Synthetic data changes the math entirely. A synthetic data provider uses your existing CAD models to generate exactly the training images your model needs, fully labeled and ready to train — so Detect-It can deploy a production-ready AI inspection system without waiting for defects to accumulate on your line.
the basics
what is synthetic data?
Traditional AI training means collecting, sorting, and hand-labeling thousands of real photos from your production line. Synthetic data skips most of that — using computer-aided design (CAD) and rendering software to generate photorealistic training images from scratch.
Start With CAD
Your engineering team already has 3D models of your parts. A synthetic data specialist uses those same CAD files — or builds them from reference images — as the foundation for every synthetic training image.
Render Realistic Images
Rendering software places your parts inside a simulated or real inspection environment, applying lighting, shadows, and surface textures that closely match what your camera sees on the line.
Train and Deploy
Because synthetic geometry is known precisely, every bounding box and annotation is generated automatically by the data provider — no manual labeling. The resulting model is handed off to Detect-It and deployed against real-world production images immediately.
when to use synthetic data
Two occasions to use
synthetic data.
Synthetic data is a tool, not a universal replacement. It excels in two specific situations that are common in industrial inspection.
Rare Defects
When a defect appears in fewer than 5% of parts, gathering enough real-world examples for reliable AI training can take months — or may never happen at all on low-volume lines. Synthetic data lets a provider generate hundreds of defect variations on demand, covering edge cases your real dataset never will.
Labeling Bottlenecks
Capturing, organizing, and hand-labeling thousands of images is expensive and slow. With synthetic data, position, orientation, and dimensions are already known — so bounding boxes and annotations are generated programmatically by the provider. What took weeks of technician time now runs automatically.
methodology
two approaches to
synthetic data.
Both methods have been validated in real-world testing. The right choice depends on how much control you want over the simulated environment.
Fully
Generated
Both the inspection environment and the parts being inspected are created entirely in CAD and rendering software. Lighting, shadows, reflections, and surface detail are all simulated computationally.
Real Background,
Generated Objects
Real photos of your inspection environment serve as the background. CAD-generated parts and defects are then composited into those real images — giving you natural lighting and surface detail in the surroundings with precise control over the inspection objects.