Computer Vision Use Case

Door Detection

Automating door detection from architectural floor plans using custom computer vision and spatial reasoning.

1. The Problem

Manually counting door arcs and verifying door types across multi-sheet floor plans takes hours and introduces human counting errors into material takeoffs.

2. Input Drawing

Architectural PDF floor plan drawing at 1/4 inch scale containing single-swing door arcs, sliding door lines, double doors, and door tags.

INPUT FORMATS: PDF (Vector / Raster) · DWG Renders · TIFF / PNG Scans

3. AI Pipeline Process

Custom YOLOv8 vision model identifies door bounding boxes, swing direction angles, and classifies door mechanisms (hinged, sliding, pocket, double).

Phase 01: Object Detection
Phase 02: 360° Rotated OCR
Phase 03: Spatial Graph Linking

4. Annotated Visual Output

Visual SVG overlay highlighting detected door arcs with color-coded bounding boxes and confidence scores (e.g. D-01: 99.2% confidence).

[VISUAL BOUNDING BOX OVERLAY & COLOR-CODED DETECTION CANVAS]

5. Structured Data Export

Structured JSON array containing door IDs, coordinates, swing orientation, room locations, and bounding box geometry.

{ "use_case": "Door Detection", "status": "success", "confidence_score": 0.984, "extracted_count": 42, "export_formats": ["JSON", "REST_API", "CSV", "EXCEL"] }

6. Business Impact

Reduces door counting time by 90% while ensuring 99%+ count accuracy for material suppliers, hardware specifiers, and trade estimators.

Frequently Asked Questions

Can the AI distinguish between single hinged doors and sliding pocket doors?

Yes. Our vision models are trained specifically to classify distinct architectural arc and line symbol conventions for hinged, pocket, bi-fold, and sliding doors.

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