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卷积神经网络（CNN）在船舶结构识别中的应用。

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

机构信息

The Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Korea.

The School of Interdisciplinary Management, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea.

出版信息

Sensors (Basel). 2022 May 18;22(10):3824. doi: 10.3390/s22103824.

DOI:10.3390/s22103824
PMID:35632233
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9145347/
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.

摘要

本文旨在研究船舶及其结构的识别，以提高从事岸对船无人机投递服务的无人机作业的安全性。本研究开发了一种系统，该系统可以使用卷积神经网络 (CNN) 区分船舶及其结构。首先，将描述海事交通管理网的数据集，并讨论 Detectron2 平台上基于 CNN 的目标感应。还将描述实验和性能。此外，本研究是基于实际的无人机投递操作进行的，这是韩国首次使用无人机进行的空中投递服务。

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