Deployment Risk Starts in ML Preparation
Most outdoor robotics programs do not fail at inference time. They fail during ML preparation. Benchmark accuracy may look acceptable but operational alignment often is not. Outdoor systems operate under seasonal shifts, low-contrast terrain, motion-induced blur, camera-specific optics, ambiguous boundaries between traversable and non-traversable zones If training and validation do not reflect these conditions, deployment […]
Real-time segmentation in outdoor environments
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AI-based granite vs dolomite sorting
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Wood optimization for pallet production
Low-grade wood is cheap — but variability, defects, and changing appearance make manual selection inconsistent. This case shows how to extract usable regions reliably at production speed. Business background What this demo demonstratesScreenshot from 2026-02-16 13-17-10 Benefits for end users What AgirVision can deliver Want something similar? Send your constraints (line speed, camera setup, defect […]