Deliver production-ready vision performance in hours, not weeks.
AUTOIQ.ai automates multi-objective ISP optimization for the STMicroelectronics STM32N6, achieving 100% ML detection accuracy while preserving professional-grade human vision aesthetics—all without touching your proprietary model.
Emmetra is STMicroelectronics (ST) Authorized Partner
Executive Summary
This case study documents how AUTOIQ.ai systematically optimizes the STM32N6 Discovery Kit camera ISP, achieving simultaneous human vision and machine vision improvements through automated multi-objective parameter tuning. The optimization demonstrates AUTOIQ.ai's core capability: intelligent exploration of ISP parameter space to discover configurations that manual sequential tuning cannot reach.
The Challenge
The default ISP configuration created conflicting optimization goals. White balance showed noticeable color casts (ΔC = 0.34), color rendering was inaccurate(ΔE = 15.735), and ML-based object detection suffered from inconsistent confidence scores and missed detections. Engineers faced a classic dilemma: manual tuning of one metric typically degraded others, requiring weeks of iterative adjustment.
The AUTOIQ.ai Approach
AUTOIQ.ai optimized 25 tunable ISP parameters across four processing blocks through 2,000 systematic iterations, measuring all human and machine vision metrics simultaneously at each step.
The Results
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White balance accuracy improved 91.1% (ΔC: 0.34 → 0.03)
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Color accuracy improved 63.4% (ΔE: 15.735 → 5.75)
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ML detection accuracy improved 100% (missed detections: 2→0, false positives: 2→0)
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ML confidence consistency improved 55.6%
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Trade-offs minimal: SNR reduction of 8.6%, exposure stability preserved
Time Impact
Optimization completed in 2-4 hours of unattended automated execution, versus 2-4 weeks of manual expert tuning.
Download the Technical Case Study
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