Automated Defect Detection

We train models to detect scratches, dents, stains, and cracks on production lines. Each defect type gets its own classification label, severity score, and bounding box annotation for full traceability.

Our camera systems perform automated size and tolerance verification against CAD specifications. We calibrate for sub-pixel accuracy and handle measurement across curved surfaces, reflective materials, and mixed geometries.

We verify consistency across production batches by analyzing color deviation (Delta E), surface texture uniformity, and coating thickness indicators. The system flags drift before it becomes a reject-level issue.

Our models identify missing components, wrong parts, reversed orientations, and incomplete assemblies. We use reference comparison against golden samples to catch deviations that traditional sensors miss.

We design inspection stations with top, side, and bottom camera views for complete part coverage. Each view runs its own optimized model, and we fuse results into a single pass/fail decision per part.

We deliver sub-100ms inference latency for line-speed inspection. Our edge-optimized models run on NVIDIA Jetson or Intel hardware, triggering reject mechanisms and logging every decision with full image evidence.

Edge GPU for real-time inference at the inspection station.

Model optimization for Intel CPU/VPU deployment.

NVIDIA inference optimizer for maximum throughput.

Cross-platform model format for portable deployment.

Primary framework for model training and experimentation.

Image preprocessing, calibration, and traditional CV pipelines.

Industrial cameras with hardware trigger and high resolution.

Structured light, backlighting, and dome lighting setups.

Purpose-built labeling workflows for defect classification.