A Review On Animal Detection Using Different Detection Techniques.
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| Title: | A Review On Animal Detection Using Different Detection Techniques. |
|---|---|
| Authors: | Sowmya, M.1, Balasubramanian, M.2, Vaidehi, K.3 |
| Source: | Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 7, p8249-8254. 6p. |
| Subject Terms: | *Machine learning, Deep learning, Object recognition (Computer vision), Computer vision, Animal behavior, Visual fields |
| Abstract: | Detecting the animals is an imperative task in the field of computer vision. The computer vision plays a prominent role in detection of various wild animals. Methods for animal detection are helpful to know about the moving behaviour of animals so as to prevent animal intrusion which results in dangerous situations in forest border area. Crop damage, Injury and loss of life of humans and wildlife are some of the impacts of human animal conflict. So there is a need to develop a system which detects the wild animal in forest border without causing any effect to human beings. Deep learning has developed as an effective machine learning method and application of deep learning has shown performance in different areas like image classification, segmentation and object detection. This paper analyses various methods of animal detection in images. A detailed survey on the methods of animal detection are performed in this paper. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 161812171 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Review On Animal Detection Using Different Detection Techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sowmya%2C+M%2E%22">Sowmya, M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Balasubramanian%2C+M%2E%22">Balasubramanian, M.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Vaidehi%2C+K%2E%22">Vaidehi, K.</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Qualitative+Inquiry%22">Turkish Online Journal of Qualitative Inquiry</searchLink>. 2021, Vol. 12 Issue 7, p8249-8254. 6p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+behavior%22">Animal behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+fields%22">Visual fields</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Detecting the animals is an imperative task in the field of computer vision. The computer vision plays a prominent role in detection of various wild animals. Methods for animal detection are helpful to know about the moving behaviour of animals so as to prevent animal intrusion which results in dangerous situations in forest border area. Crop damage, Injury and loss of life of humans and wildlife are some of the impacts of human animal conflict. So there is a need to develop a system which detects the wild animal in forest border without causing any effect to human beings. Deep learning has developed as an effective machine learning method and application of deep learning has shown performance in different areas like image classification, segmentation and object detection. This paper analyses various methods of animal detection in images. A detailed survey on the methods of animal detection are performed in this paper. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 8249 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Animal behavior Type: general – SubjectFull: Visual fields Type: general Titles: – TitleFull: A Review On Animal Detection Using Different Detection Techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sowmya, M. – PersonEntity: Name: NameFull: Balasubramanian, M. – PersonEntity: Name: NameFull: Vaidehi, K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13096591 Numbering: – Type: volume Value: 12 – Type: issue Value: 7 Titles: – TitleFull: Turkish Online Journal of Qualitative Inquiry Type: main |
| ResultId | 1 |