Underwater object detection and datasets: a survey
Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as wat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jian, Muwei [verfasserIn] |
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| Format: |
E-Artikel |
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Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Intelligent Marine Technology and Systems - Springer Nature Singapore, 2023, 2(2024), 1 vom: 04. März |
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| Übergeordnetes Werk: |
volume:2 ; year:2024 ; number:1 ; day:04 ; month:03 |
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10.1007/s44295-024-00023-6 |
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| 520 | |a Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. | ||
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10.1007/s44295-024-00023-6 doi (DE-627)SPR055022723 (SPR)s44295-024-00023-6-e DE-627 ger DE-627 rakwb eng Jian, Muwei verfasserin (orcid)0000-0002-4249-2264 aut Underwater object detection and datasets: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. Underwater images (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Underwater dataset (dpeaa)DE-He213 Marine internet of things (dpeaa)DE-He213 Yang, Nan aut Tao, Chen aut Zhi, Huixiang aut Luo, Hanjiang aut Enthalten in Intelligent Marine Technology and Systems Springer Nature Singapore, 2023 2(2024), 1 vom: 04. März (DE-627)1870442482 2948-1953 nnns volume:2 year:2024 number:1 day:04 month:03 https://dx.doi.org/10.1007/s44295-024-00023-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 2 2024 1 04 03 |
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10.1007/s44295-024-00023-6 doi (DE-627)SPR055022723 (SPR)s44295-024-00023-6-e DE-627 ger DE-627 rakwb eng Jian, Muwei verfasserin (orcid)0000-0002-4249-2264 aut Underwater object detection and datasets: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. Underwater images (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Underwater dataset (dpeaa)DE-He213 Marine internet of things (dpeaa)DE-He213 Yang, Nan aut Tao, Chen aut Zhi, Huixiang aut Luo, Hanjiang aut Enthalten in Intelligent Marine Technology and Systems Springer Nature Singapore, 2023 2(2024), 1 vom: 04. März (DE-627)1870442482 2948-1953 nnns volume:2 year:2024 number:1 day:04 month:03 https://dx.doi.org/10.1007/s44295-024-00023-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 2 2024 1 04 03 |
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10.1007/s44295-024-00023-6 doi (DE-627)SPR055022723 (SPR)s44295-024-00023-6-e DE-627 ger DE-627 rakwb eng Jian, Muwei verfasserin (orcid)0000-0002-4249-2264 aut Underwater object detection and datasets: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. Underwater images (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Underwater dataset (dpeaa)DE-He213 Marine internet of things (dpeaa)DE-He213 Yang, Nan aut Tao, Chen aut Zhi, Huixiang aut Luo, Hanjiang aut Enthalten in Intelligent Marine Technology and Systems Springer Nature Singapore, 2023 2(2024), 1 vom: 04. März (DE-627)1870442482 2948-1953 nnns volume:2 year:2024 number:1 day:04 month:03 https://dx.doi.org/10.1007/s44295-024-00023-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 2 2024 1 04 03 |
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10.1007/s44295-024-00023-6 doi (DE-627)SPR055022723 (SPR)s44295-024-00023-6-e DE-627 ger DE-627 rakwb eng Jian, Muwei verfasserin (orcid)0000-0002-4249-2264 aut Underwater object detection and datasets: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. Underwater images (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Underwater dataset (dpeaa)DE-He213 Marine internet of things (dpeaa)DE-He213 Yang, Nan aut Tao, Chen aut Zhi, Huixiang aut Luo, Hanjiang aut Enthalten in Intelligent Marine Technology and Systems Springer Nature Singapore, 2023 2(2024), 1 vom: 04. März (DE-627)1870442482 2948-1953 nnns volume:2 year:2024 number:1 day:04 month:03 https://dx.doi.org/10.1007/s44295-024-00023-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 2 2024 1 04 03 |
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Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. © The Author(s) 2024 |
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Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. © The Author(s) 2024 |
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Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond. © The Author(s) 2024 |
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| doi_str |
10.1007/s44295-024-00023-6 |
| up_date |
2024-07-04T03:53:10.343Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055022723</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240305064716.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240305s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s44295-024-00023-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055022723</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s44295-024-00023-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jian, Muwei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4249-2264</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Underwater object detection and datasets: a survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. 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