Artificial intelligence-based ship hull plate corrosion monitoring using Convolutional Neural Network (CNN): comparison of YOLOv8 and Detectron2 architecture models-Reference-Cited by-同舟云学术

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Artificial intelligence-based ship hull plate corrosion monitoring using Convolutional Neural Network (CNN): comparison of YOLOv8 and Detectron2 architecture models

Published:2024-09-02 Issue: Volume: Page:1-11
ISSN:0937-7255
Container-title:Ship Technology Research
language:en
Short-container-title:Ship Technology Research

Author:

Tuswan Tuswan¹,Veda Kadek Arya Bisma¹,Chrismianto Deddy¹,Mursid Ocid¹²,Adam Safri³,Prabowo Aditya Rio⁴,Nubli Haris⁵

Affiliation:

1. Departement of Naval Architecture, Universitas Diponegoro, Semarang, Indonesia

2. Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, United Kingdom

3. Department of Electrical Engineering, Politeknik Negeri Pontianak, Pontianak, Indonesia

4. Department of Mechanical Engineering, Universitas Sebelas Maret, Surakarta, Indonesia

5. Department of Naval Architecture and Marine System Engineering, Pukyong National University, South Korea

Funder

Riset Publikasi Internasional

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09377255.2024.2397608

Reference47 articles.

1. Corrosion Detection for Automated Visual Inspection

2. Multiple Endocrine Neoplasia Type 1 Presenting with an Invasive Giant Prolactinoma

3. MRI-based brain tumour image detection using CNN based deep learning method

4. Optimal inspections and maintenance planning for anti-corrosion coating failure on ships using non-homogeneous Poisson Processes

5. Review on uncertainties in fatigue loads and fatigue life of ships and offshore structures

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