Computer Vision Enabled Yard Management System – Zero-Cost POC

The Computer Vision Enabled Yard Management System – Zero-Cost Proof of Concept (PoC) by NAVA Software Solutions helps logistics, manufacturing, intermodal, port, retail, and distribution organizations achieve real-time visibility and operational optimization across their yards, terminals and distribution centers.

Built on AWS’s proven cloud infrastructure and NAVA’s deep expertise in AI-driven logistics automation, this PoC demonstrates how Computer Vision (CV) and Machine Learning (ML) can intelligently track trucks, trailers, containers, reefers, and other yard equipment, reducing dwell times, detention/demurrage costs, theft risks, improving throughput, and enhancing on-ground safety.

The solution integrates AWS services such as Kinesis Video Streams, SageMaker, IoT Core, Lambda, CloudFront, DynamoDB, and QuickSight to deliver a unified, data-driven layer for automated yard visibility and decision-making. It also supports edge deployments powered by NVIDIA Jetson or GPU-enabled gateways, enabling low-latency inference and real-time responsiveness even in limited-connectivity environments.

Discovery & Assessment – Conduct discovery sessions to understand current yard or port operations, logistics systems, and performance KPIs.

Data Pipeline Setup – Configure AWS services (S3, IoT Core, Lambda, Glue, Kinesis) for ingesting and processing live or historical video data.

Model Training – Use Amazon SageMaker to train custom computer vision models for detecting and classifying equipment and other use cases.

**Edge or Cloud Inference Deployment **

Cloud Mode: Stream camera feeds via AWS Kinesis Video Streams and process them in SageMaker or Lookout for Vision.

Edge Mode: Deploy inference models locally using AWS IoT Greengrass or SageMaker Edge Manager for real-time detection at the yard.

Visualization Dashboards – Build dashboards hosted on Amazon CloudFront with analytics powered by Amazon QuickSight.

Insight Demonstration – Deliver reports on asset movement, dwell time, and utilization for data-driven decision-making.

Phase 1 – Discovery & Setup (Week 1)

Conduct requirement workshops, finalize architecture, and configure AWS environment.

Phase 2 – Model Development (Weeks 2–3)

• Annotate sample video data and train vision models using Amazon SageMaker.
• Validate accuracy for vehicle, container, and equipment detection with customer-provided data.

Phase 3 – Deployment & Visualization (Week 4)

Deploy inference pipeline based on customer preference:

Cloud Mode: Stream camera feeds via AWS Kinesis Video Streams and process them in Amazon SageMaker or Amazon Lookout for Vision for centralized inference and analytics.

Edge Mode: Deploy optimized computer vision models using AWS IoT Greengrass or SageMaker Edge Manager on NVIDIA Jetson or GPU-enabled edge gateways, enabling real-time detection and alerting directly at the yard or terminal.

Launch QuickSight dashboards and CloudFront-based visualization interface.

Phase 4 – Demonstration & Handover

• Live demonstration of real-time tracking, dwell analytics, and alerts.
• Deliver documentation, findings, and recommendations for production rollout.

All infrastructure is built using AWS managed and serverless services for scalability, cost-efficiency, and security.