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Container Defects Detection System for a Global Port Operator

Simplifying Technology Strengthening Business > AI Technology > Container Defects Detection System for a Global Port Operator
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Container Defects Detection System for a Global Port Operator

Automating defect detection in shipping containers using deep learning models (MobileNetV2 & YOLOv8) to improve accuracy, reduce manual effort, and enhance real-time processing efficiency.

Category: AI/ML
Industry: Logistics & Transportation.

Project Info

  • Client:

    Digital Health Platform | UAE

  • Services:

    Credit Restoration

  • Date:

    February 12, 2024

  • Category:

    Finance

  • Team:

    Jonathan Hunt

Business Objective/Challenges:

  • Automate the inspection process for shipping container defects – Implement automated systems to streamline defect identification.
  • Reduce the labor-intensive and time-consuming nature of manual container inspections – Cut down on hours spent by human inspectors and speed up throughput.
  • Minimize human error in defect detection due to task complexity and repetition – Use consistent automated checks to lower missed or misclassified defects.
  • Improve the accuracy of identifying various types of container defects – Leverage advanced detection algorithms to spot corrosion, dents, cracks, and seal failures precisely.
  • Prevent supply chain disruptions through timely defect detection – Catch issues early to avoid delays and rerouting of shipments.
  • Protect transported goods by ensuring container integrity – Maintain cargo safety by ensuring containers meet required standards before transit.
  • Scale inspection capabilities to match increasing container traffic – Deploy automated solutions that can handle higher volumes without proportional labor increases.
  • Implement a revolutionary solution for handling complex defect detection requirements – Adopt AI-driven, sensor-fusion, or vision-based systems to meet sophisticated inspection needs.

Solution/Approach

  • Developed an automated system to detect defects in shipping containers.
  • The system uses a deep learning model to detect defects in the containers.
  • Collected a dataset of more than 7,500+ container images to train and evaluate the deep learning model.
  • The system classifies container defects into five categories: Deframe, Hole, Major Dent, Minor Dent, and Rust.
  • The system performs real-time inspection of container images and videos for quick and efficient detection.
  • Manual inspections are replaced with automated processes, increasing accuracy in defect identification.

Technologies

  • Artificial Intelligence, Machine Learning, Deep Learning, Image Classification, Object Detection

System Architecture:

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Business Outcome :

  • The model integration combines the trained models to detect defects in the container.
  • The system streamlines the container inspection process, reducing time and resource requirements.
  • The solution eliminates the risk of missed defects that often occur during manual inspections.
  • The system rapidly identifies defects as containers are processed, preventing defective containers from entering the supply chain.
  • The solution reduces operational costs by optimizing resource allocation and preventing potential damage.
  • The system increases container throughput, minimizing congestion and delays in the supply chain.
  • The solution can be integrated into existing container handling processes and scaled to accommodate growing needs.