Recognizing 5,000+ Control Loops Using Just 3 Templates
By Anand George
Challenge
The client’s project involved standard utility packages where P&IDs often repeated the same control loop structure: a valve, bypass line, and pressure transmitter — repeated with minor variations across hundreds of drawings.
Despite this repetition, engineers were manually reviewing every loop, due to:
- No metadata in the PDFs
- Slight differences in tag positions or line angles
- Inconsistent vendor drawing practices
With 5,000+ such arrangements to verify, manual review became a bottleneck.
“We knew these loops were repeating — but we had no way to programmatically recognize them.”
Solution
Storm Consulting implemented a template matching system that allowed the client to:
- Select and save 3 known control loop patterns as templates
- Automatically scan new P&IDs (PDF or image) for visual matches
- Annotate matched blocks with predefined metadata (loop ID, components)
- Flag unmatched regions for manual review
Implementation Highlights
- Used OpenCV’s
matchTemplate
+ non-max suppression to locate all matches per P&ID - Incorporated rotation and scale invariance for higher match accuracy
- Allowed engineers to upload new templates as needed (no coding required)
- Matched loops were annotated as bounding boxes + component list
- Matching results were exported as both images and CSV data
Results
Metric | Manual Review Estimate | Template Matching Output |
---|---|---|
Repeated control loop instances | ~5,200 | 5,120 detected |
Engineer-hours saved | 180–200 | ~20 (for review only) |
Template creation time (3 total) | ~45 minutes | One-time effort |
False positive rate | ~3% | Corrected via review UI |
Integration with annotation workflow | N/A | Fully integrated |
“Once the first match came through, our team’s confidence skyrocketed. It was like a ‘find and fill’ function for our P&IDs.” — Instrumentation Lead, Client Project Team
Why It Worked
- Most control loop structures varied only in tag and orientation — perfect for template recognition
- Engineers could expand the template library themselves — no dev cycles needed
- Matched results retained visual + semantic context (not just boxes)
- Minimal tuning — high match accuracy out of the box with grayscale + threshold preprocessing