Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 9(3) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3441-3450
S&M3403 Research Paper of Special Issue https://doi.org/10.18494/SAM4477 Published: September 29, 2023 Marine Debris Detection Using Optimized You Only Look Once Version 5 [PDF] Tae-Young Lee, Seung Bae Jeon, and Myeong-Hun Jeong (Received April 27, 2023; Accepted August 8, 2023) Keywords: marine debris, object detection, marine observation, deep learning, YOLOv5
Marine debris is one of the most widespread pollution problems facing oceans and waterways. It threatens the marine ecosystem and navigation safety. Thus, the extensive and timely monitoring of marine debris is crucial. In this study, we aimed to detect marine debris from images. You Only Look Once Version 5 (YOLOv5) was used to train a model to detect marine debris. Our experimental results reveal that the model achieves a mean average precision (mAP) of 96.8% and an inference speed of 1.3 ms per image. The optimized YOLOv5 has a higher mAP than a faster region-based convolutional neural network and requires less inference time than a single-shot multibox detector.
Corresponding author: Myeong-Hun Jeong   This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Tae-Young Lee, Seung Bae Jeon, and Myeong-Hun Jeong , Marine Debris Detection Using Optimized You Only Look Once Version 5, Sens. Mater., Vol. 35, No. 9, 2023, p. 3441-3450. |
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