Low-latency container number recognition for automatic shipping

Logistics DevelopmentEU

Company

NDA

Industry

Logistics

Country of the Company

EU

Type of Service

Development

Tasks

  • Automate the recognition of shipping container serial numbers.
  • Replace manual data entry processes to reduce operational errors.
  • Build a solution optimized for edge deployment with minimal computational resources.
  • Ensure high recognition accuracy and minimal latency in real-time logistics environments.

Challenges

  • Offered solution needed to be capable of running reliably on low-power edge devices in high-throughput environments.
  • The recognition system also had to meet strict accuracy standards, as manual errors in serial number entry led to shipment delays and inventory mismatches.

Solutions

  • Designed an OCR system tailored to edge devices, with lightweight, efficient algorithms.
  • Focused on optimizing text detection and recognition to meet low-latency requirements.
  • Implemented a dual-validation mechanism to ensure container serial number accuracy, significantly reducing the risk of incorrect readings.
  • Developed a user-friendly interface for easy integration with the client’s existing container tracking system.

Outcomes

  • Accelerated the logistics workflow, cutting time spent on container verification by 70%.
  • Reduced manual entry errors, improving data integrity and operational efficiency.
  • Enabled on-site, real-time container identification without reliance on cloud processing.
  • Delivered a scalable solution that can be deployed across multiple facilities with minimal infrastructure changes

Technologies Used

Python

YOLO v8

YOLO v11

OpenCV

pyTorch

SORT

Company

NDA

Industry

Logistics

Country of the Company

EU

Type of Service

Development

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