Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles

With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. How...

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Autores principales:	Gen Zheng, Jianhu Zhao, Shaobo Li, Jie Feng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sub-bottom profiler pipeline detection YOLOv5s zero-shot Science Q
Acceso en línea:	https://doaj.org/article/a9e6144e15a04f389958b3deb25ad5ce
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spelling	oai:doaj.org-article:a9e6144e15a04f389958b3deb25ad5ce2021-11-11T18:55:34ZZero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles10.3390/rs132144012072-4292https://doaj.org/article/a9e6144e15a04f389958b3deb25ad5ce2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4401https://doaj.org/toc/2072-4292With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.Gen ZhengJianhu ZhaoShaobo LiJie FengMDPI AGarticlesub-bottom profilerpipeline detectionYOLOv5szero-shotScienceQENRemote Sensing, Vol 13, Iss 4401, p 4401 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sub-bottom profiler pipeline detection YOLOv5s zero-shot Science Q
spellingShingle	sub-bottom profiler pipeline detection YOLOv5s zero-shot Science Q Gen Zheng Jianhu Zhao Shaobo Li Jie Feng Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
description	With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.
format	article
author	Gen Zheng Jianhu Zhao Shaobo Li Jie Feng
author_facet	Gen Zheng Jianhu Zhao Shaobo Li Jie Feng
author_sort	Gen Zheng
title	Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
title_short	Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
title_full	Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
title_fullStr	Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
title_full_unstemmed	Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
title_sort	zero-shot pipeline detection for sub-bottom profiler data based on imaging principles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a9e6144e15a04f389958b3deb25ad5ce
work_keys_str_mv	AT genzheng zeroshotpipelinedetectionforsubbottomprofilerdatabasedonimagingprinciples AT jianhuzhao zeroshotpipelinedetectionforsubbottomprofilerdatabasedonimagingprinciples AT shaoboli zeroshotpipelinedetectionforsubbottomprofilerdatabasedonimagingprinciples AT jiefeng zeroshotpipelinedetectionforsubbottomprofilerdatabasedonimagingprinciples
_version_	1718431664911155200

Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles

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