G-Net: An Efficient Convolutional Network for Underwater Object Detection

Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the...
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Autor*in:	Xiaoyang Zhao [verfasserIn] Zhuo Wang [verfasserIn] Zhongchao Deng [verfasserIn] Hongde Qin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	image enhancement image reconstruction underwater object detection feature enhancement

Übergeordnetes Werk:	In: Journal of Marine Science and Engineering - MDPI AG, 2014, 12(2024), 1, p 116
Übergeordnetes Werk:	volume:12 ; year:2024 ; number:1, p 116

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jmse12010116

Katalog-ID:	DOAJ096332999

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520			\|a Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement with target detection. Different from the traditional method of reconstructing enhanced images for target detection, G-Net realizes the integration of image enhancement and target detection. In addition, our feature map learning module (FML) can effectively extract defogging features. The test results in a real underwater environment show that G-Net improves the detection accuracy of underwater targets by about 5%, but also has high detection efficiency, which ensures the reliability of underwater robots in seabed investigation and aquaculture activities.
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language	English
source	In Journal of Marine Science and Engineering 12(2024), 1, p 116 volume:12 year:2024 number:1, p 116
sourceStr	In Journal of Marine Science and Engineering 12(2024), 1, p 116 volume:12 year:2024 number:1, p 116
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	image enhancement image reconstruction underwater object detection feature enhancement Naval architecture. Shipbuilding. Marine engineering Oceanography
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container_title	Journal of Marine Science and Engineering
authorswithroles_txt_mv	Xiaoyang Zhao @@aut@@ Zhuo Wang @@aut@@ Zhongchao Deng @@aut@@ Hongde Qin @@aut@@
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callnumber-first	V - Naval Science
author	Xiaoyang Zhao
spellingShingle	Xiaoyang Zhao misc VM1-989 misc GC1-1581 misc image enhancement misc image reconstruction misc underwater object detection misc feature enhancement misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography G-Net: An Efficient Convolutional Network for Underwater Object Detection
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topic	misc VM1-989 misc GC1-1581 misc image enhancement misc image reconstruction misc underwater object detection misc feature enhancement misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography
topic_unstemmed	misc VM1-989 misc GC1-1581 misc image enhancement misc image reconstruction misc underwater object detection misc feature enhancement misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography
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title	G-Net: An Efficient Convolutional Network for Underwater Object Detection
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title_auth	G-Net: An Efficient Convolutional Network for Underwater Object Detection
abstract	Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement with target detection. Different from the traditional method of reconstructing enhanced images for target detection, G-Net realizes the integration of image enhancement and target detection. In addition, our feature map learning module (FML) can effectively extract defogging features. The test results in a real underwater environment show that G-Net improves the detection accuracy of underwater targets by about 5%, but also has high detection efficiency, which ensures the reliability of underwater robots in seabed investigation and aquaculture activities.
abstractGer	Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement with target detection. Different from the traditional method of reconstructing enhanced images for target detection, G-Net realizes the integration of image enhancement and target detection. In addition, our feature map learning module (FML) can effectively extract defogging features. The test results in a real underwater environment show that G-Net improves the detection accuracy of underwater targets by about 5%, but also has high detection efficiency, which ensures the reliability of underwater robots in seabed investigation and aquaculture activities.
abstract_unstemmed	Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement with target detection. Different from the traditional method of reconstructing enhanced images for target detection, G-Net realizes the integration of image enhancement and target detection. In addition, our feature map learning module (FML) can effectively extract defogging features. The test results in a real underwater environment show that G-Net improves the detection accuracy of underwater targets by about 5%, but also has high detection efficiency, which ensures the reliability of underwater robots in seabed investigation and aquaculture activities.
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title_short	G-Net: An Efficient Convolutional Network for Underwater Object Detection
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