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Application of Convolutional Neural Network (CNN) to Recognize Ship Structures.

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Summary
This summary is machine-generated.

This study enhances drone delivery safety by using a convolutional neural network (CNN) to recognize ships and structures. The system accurately identifies maritime objects, improving navigation for shore-to-ship drone operations.

Keywords:
convolutional neural network (CNN)faster R-CNNmask R-CNNrecognize ship structures

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Area of Science:

  • Maritime technology
  • Artificial intelligence in logistics
  • Robotics and automation

Background:

  • Increasing use of drones for maritime logistics requires enhanced safety protocols.
  • Accurate identification of vessels and structures is critical for autonomous drone navigation.
  • Existing systems may lack the precision needed for complex shore-to-ship delivery environments.

Purpose of the Study:

  • To develop and evaluate a system for recognizing ships and their structures to enhance drone delivery safety.
  • To improve the reliability of autonomous drone operations in maritime settings.
  • To support the first air delivery service by drones in Korea.

Main Methods:

  • Utilized a convolutional neural network (CNN) for object detection and recognition.
  • Employed the Detectron2 platform for CNN-based object sensing.
  • Developed a dataset from the Marine Traffic Management Net for training and validation.

Main Results:

  • The developed CNN system demonstrated effective recognition of ships and their structures.
  • Performance metrics indicate high accuracy in distinguishing maritime objects.
  • The system was validated through actual drone delivery operations.

Conclusions:

  • The CNN-based recognition system significantly improves the safety of drone operations for shore-to-ship delivery.
  • Accurate ship and structure identification is feasible with advanced AI models.
  • This technology paves the way for safer and more efficient autonomous maritime drone services.