Visual Quality Control of Field Installations
The Challenge
Missing critical components — screws, labels, closures — went unnoticed during field installation. Identifying defects after the fact required sending another team, multiplying logistics costs. Manual certification of thousands of technician photos was not scalable.
What We Built
Deep learning classification models verify infrastructure assembly across 11 component classes (Brackets, Splice Trays, Cable Labels and more) regardless of angle or lighting. Anomaly detection alerts fire immediately when missing items are detected — before the technician leaves the area.
Operational Impact
Instant node certification speeds up operator billing. Elimination of corrective second visits protects gross margin. Thousands of enclosures per day audited without increasing supervision headcount.
Technology Used
Python
PyTorch
OpenCV
AWS Rekognition
FastAPI
