Rail Yard Digital Twin Case Study

Client: Major US Rail Maintenance Operator Role: Trifork served as implementation partner and AI platform developer

Project Overview

Autonomous drone monitoring system for rail yard asset inspection using DJI Dock 3 and Matrice 4D - the identical hardware stack specified in YPF's RFP.

Capability Comparison

Capability	Rail Yard Deployment	YPF Requirement
Hardware	DJI Dock 3 + Matrice 4D	DJI Dock 3 + Matrice 4TD/400
Flight Management	DJI FlightHub 2	DJI FlightHub 2
Operations	BVLOS under FAA waivers	BVLOS under ANAC
Accuracy	92-98% OCR in industrial conditions	≥95% detection accuracy
Processing	~30 min end-to-end	≤20 min analytics
Coverage	100-200 acres with 4 docks	Vaca Muerta facilities
Operations	Night flights with spotlight	24/7 monitoring
Integration	API to enterprise systems	SAP PM, SCADA

Technical Architecture

Vision AI Ensemble

┌─────────────────────────────────────────────────────────────────────┐
│                    NVIDIA Triton Inference Server                    │
│                                                                      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐              │
│  │ GroundingDINO│→│     SAM2     │→│ AWS Rekognition│             │
│  │  Detection   │  │ Segmentation │  │     OCR       │             │
│  └──────────────┘  └──────────────┘  └──────────────┘              │
└─────────────────────────────────────────────────────────────────────┘

• GroundingDINO: Zero-shot object detection with text prompts
• SAM2: Precise segmentation for detected objects
• AWS Rekognition: Text extraction with confidence scoring

Probabilistic OCR Reconciliation

Multi-frame consensus voting with confidence weighting:

1. Extract text from multiple frames of same asset
2. Weight by detection confidence
3. Reconcile to single canonical reading
4. Flag low-confidence for manual review

Digital Twin Storage

• PostGIS: Geospatial asset correlation
• Time-series: Historical tracking
• Asset linking: Detection events correlated to physical assets

Achieved Results

| Metric | Result | | ------------------- | --------------------------------------- | --------------------------------------- | | OCR Accuracy | 92-98% in industrial outdoor conditions | | | Processing Latency | ~30 minutes end-to-end | | | False Positive Rate | <5% after correlation | | | Coverage | 100-200 acres per mission | | | Uptime | Production-level reliability |

Transferable Components

Direct Transfer to YPF

1. Triton Ensemble Pattern - Same model orchestration
2. PostGIS Digital Twin - Asset correlation approach
3. Micro-batch Processing - Near-real-time pipeline
4. Hybrid Clustering - Multi-signal data fusion
5. FlightHub 2 Integration - API patterns

Adaptation Required

1. Sensor Integration - Add methane, GPR, LiDAR
2. Use Case Models - Train for oil & gas assets
3. Correlation Logic - Multi-sensor fusion
4. SLA Optimization - 20-min target vs. 30-min achieved

Lessons Learned

Challenge	Solution	YPF Application
Wind impact on accuracy	Multi-frame consensus	Apply to all visual detection
Night lighting variation	Adaptive exposure	Thermal + spotlight combination
Large asset diversity	Zero-shot detection	Flexible for O&G equipment
Enterprise integration	API-first design	SAP PM connector pattern