Prevent Disruptions, Reduce Costs: AI Pallet Detection!

[biapalmeiro]

Rajesh Kurusetty shared a super cool post on Linkedin I wanted to add here as well. What are your thoughts about that? Leave us a comment or question.

How can warehouse operators reduce the costly disruptions caused by defective pallets, which force manual inspections and rework, costing extensive warehouse operations millions annually?

Pallet Defect Detection, a key component of the Manufacturing AI Suite running on Intel compute, delivers significant operational savings through real-time, automated quality control at the edge.

Using AI-driven vision systems, the solution monitors pallet conditions in real time through video streams from cameras mounted on robots or forklifts and a network of cameras installed on warehouse walls and ceilings. This dual-camera approach enables comprehensive real-time detection of defects as pallets are loaded, unloaded, and moved throughout the warehouse, facilitating faster decision-making and significantly reducing the risk of damaged goods entering the supply chain.

Key Features:

• Real-time AI-assisted defect detection
• Low-latency, edge data processing on an IPC or Xeon Server based on # of video streams processed in parallel
• On-premise analytics for data privacy and bandwidth efficiency
• Scalable solution seamlessly operates across single or distributed large warehouse networks

How It Works:
The solution runs on a microservices-based architecture, including DL Streamer Pipeline Server, Model Registry, MediaMTX, Coturn, OpenTelemetry, Prometheus, Postgres, and Minio. It supports REST-based pipeline control, WebRTC video streaming, and AI model management—all optimized for edge deployment.

Get started today with our open-source implementation. Deploy quickly, customize easily, and automate your warehouse quality control with confidence.
https://github.com/open-edge-platform/edge-ai-suites/tree/main/manufacturing-ai-suite/pallet-defect-detection