Case Study: AI Powered Container Number & License Plate Recognition System

Efficiently managing the inflow and outflow of shipping containers in ports, warehouses, and logistics yards is a complex task. Manual data entry of container numbers and vehicle license plates leads to inefficiencies, errors, and security gaps.

BioEnable Technologies developed a custom AI-powered recognition solution to automatically detect and match shipping container numbers with associated vehicle license plates (ALPR), streamlining the entry/exit verification process and ensuring end-to-end traceability.

• Difficulty in manually tracking container movements.

• Lack of real-time matching between containers and transport vehicles.

• Frequent delays in gate processing due to human verification bottlenecks.

• Visual complexity due to dirty, occluded, or non-standard container number fonts.

• Regional variations in vehicle license plates, requiring adaptable models.

BioEnable deployed an AI-based Container & Vehicle Recognition System comprising two main modules:

1. Container Number Recognition

• Developed a custom-trained OCR model, fine-tuned specifically for ISO 6346 container numbering formats.

• Used synthetic data augmentation and real-world datasets collected from yards and ports to train the model.

• Incorporated detection capabilities to locate container numbers from different positions: side-view, front-view, top-view.

2. Automatic License Plate Recognition (ALPR)

• Trained a separate deep learning-based ALPR model to detect and decode vehicle license plates.

• Capable of handling multi-country plate formats, poor lighting, dirt, motion blur, and angle distortions.

• Integrated with local traffic and yard management regulations.

3. AI-Based Matching & Correlation

• The system co-relates container numbers with the license plates of carrier vehicles entering/exiting the premises.

• Ensures that only authorized containers are allowed in/out with the correct vehicle, preventing theft or misrouting.

• Includes timestamped records and gate camera footage linking both recognitions.

• High-accuracy AI models (>95%) for container number & ALPR recognition.

• Works with existing CCTV/IP camera feeds.

• Real-time alerts for mismatched container-vehicle entries.

• Integrated dashboard for log viewing, searching, and exporting reports.

• Supports edge and cloud deployments.

• Deep Learning Models: YOLOv5, CRNN-based OCR, OpenALPR enhancements.

• Frameworks: PyTorch, TensorFlow.

• Video Analytics: OpenCV, GStreamer pipeline integration.

• Backend: Python Flask, Node.js API layer.

• UI/UX: ReactJS-based web dashboard.

This AI-powered system showcases the power of domain-agnostic AI models tailored to logistics needs. By leveraging custom-trained models and video intelligence, BioEnable Technologies transformed traditional container tracking into a secure, fast, and intelligent process, future-proofing yard and port operations.

For information visit: https://www.bioenabletech.com/container-number-recognition-system