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

Journal: Sensors (Basel, Switzerland)
Published Date: May 18, 2022

Abstract

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN's object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations-the first air delivery service by drones in Korea.

Authors

  • Jae-Jun Lim
    The Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Korea.
  • Dae-Won Kim
    The School of Interdisciplinary Management, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea.
  • Woon-Hee Hong
    The Department of Radio Engineering, Korea Maritime & Ocean University, Busan 49112, Korea.
  • Min Kim
    Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Dong-Hoon Lee
    Department of Orthopedic Surgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea.
  • Sun-Young Kim
    Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi, South Korea.
  • Jae-Hoon Jeong
    The School of IT, Information and Control Engineering, Kunsan National University, Gunsan 54150, Korea.

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