Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different s...

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Autores principales:	Chin-Chun Chang, Yen-Po Wang, Shyi-Chyi Cheng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fish segmentation sonar images conditional random fields mask R-CNN Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/43df252520b94720909b503ebd6f787c
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id	oai:doaj.org-article:43df252520b94720909b503ebd6f787c
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spelling	oai:doaj.org-article:43df252520b94720909b503ebd6f787c2021-11-25T18:58:01ZFish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields10.3390/s212276251424-8220https://doaj.org/article/43df252520b94720909b503ebd6f787c2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7625https://doaj.org/toc/1424-8220Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.Chin-Chun ChangYen-Po WangShyi-Chyi ChengMDPI AGarticlefish segmentationsonar imagesconditional random fieldsmask R-CNNChemical technologyTP1-1185ENSensors, Vol 21, Iss 7625, p 7625 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fish segmentation sonar images conditional random fields mask R-CNN Chemical technology TP1-1185
spellingShingle	fish segmentation sonar images conditional random fields mask R-CNN Chemical technology TP1-1185 Chin-Chun Chang Yen-Po Wang Shyi-Chyi Cheng Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
description	Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.
format	article
author	Chin-Chun Chang Yen-Po Wang Shyi-Chyi Cheng
author_facet	Chin-Chun Chang Yen-Po Wang Shyi-Chyi Cheng
author_sort	Chin-Chun Chang
title	Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
title_short	Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
title_full	Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
title_fullStr	Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
title_full_unstemmed	Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields
title_sort	fish segmentation in sonar images by mask r-cnn on feature maps of conditional random fields
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/43df252520b94720909b503ebd6f787c
work_keys_str_mv	AT chinchunchang fishsegmentationinsonarimagesbymaskrcnnonfeaturemapsofconditionalrandomfields AT yenpowang fishsegmentationinsonarimagesbymaskrcnnonfeaturemapsofconditionalrandomfields AT shyichyicheng fishsegmentationinsonarimagesbymaskrcnnonfeaturemapsofconditionalrandomfields
_version_	1718410458736623616

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

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