LiDAR Safety Monitoring
Applies perception, calibration, and operational thresholds to safety monitoring where reliability matters more than demo accuracy.
system visual
Protected or source media unavailable. Architecture preview used instead.
System Overview
3D computer vision safety monitoring pattern for operational environments using LiDAR-style perception and false-positive controls.
AI Solutions Engineer; enterprise details abstracted.
Implementation Signals
System flow
A compact view of how inputs move through processing, orchestration, validation, and output.
3D Sensor Input
Calibration
Perception Model
Safety Thresholds
Operator Signal
Engineering decisions
Decision Record
Optimize for operational false positives
Problem: Safety systems fail when operators stop trusting alerts.
Approach: Treat false positives, calibration, and environment thresholds as first-class system design concerns.
Tradeoff: More tuning and monitoring work than a standalone model demo.
Outcome: Resume-backed work reduced false positives to fewer than one per day.
Results / Learnings
Detection
>98% detection accuracy, resume-provided.
Noise
False positives reduced to <1/day, resume-provided.
Progressive depth
This page keeps the outcome and architecture visible first. Implementation stack, decisions, constraints, and media are available below so technical depth is opt-in rather than forced.
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Repo: Package notes document RAFT, YOLO, KITTI evaluation, and Pi Camera streaming.
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README: States high accuracy and precision, but no exact numeric value is provided.
Open