Precast Concrete AI Inventory System
An AI-powered inventory monitoring platform for precast concrete manufacturing yards - combining YOLO object detection with Excel-based records to automate stock counting, validate inventory, and surface discrepancies through a real-time operations dashboard.
Key Outcome
85%+ detection accuracy
Automated yard counting replacing manual stock verification with real-time AI-powered inventory visibility.
Precast concrete manufacturers rely on manual yard counts to verify stock - a slow, error-prone process that leads to discrepancies, delayed shipments, and poor inventory visibility across large outdoor storage areas.
Built a computer vision pipeline using YOLO (via Roboflow) to detect and classify precast units from camera feeds, drones, and mobile images. A counting engine applies confidence filtering and NMS to aggregate SKU-level counts, compares them against Excel inventory records, and surfaces discrepancies and anomalies through a React/Next.js operations dashboard with live KPIs, analytics charts, and shipment tracking.
Key Features
YOLO-based detection and SKU classification of precast concrete units from camera or drone images
Confidence filtering and Non-Maximum Suppression for accurate per-SKU unit counts
Automated comparison of AI counts against Excel inventory records to flag discrepancies
Executive KPI dashboard: total inventory, inbound/outbound movements, net movement, shipment-ready stock
Inventory analytics: distribution charts, stock trends, SKU performance, movement analysis
AI inference results panel showing detection outputs, object counts, and confidence levels
Discrepancy monitoring module highlighting inventory mismatches requiring review
Roboflow-hosted model with dataset management, annotation, and cloud inference API
Technology Stack
Project Visuals
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