AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network
| Title: | AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network |
|---|---|
| Authors: | Ma, Xiaolin, Cheng, Junkai, Li, Aihua, Zhang, Yuhua, Lin, Zhilong |
| Publication Year: | 2024 |
| Collection: | Computer Science |
| Subject Terms: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, 68T45, I.2.10 |
| More Details: | Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. Despite the development of numerous ship detection methodologies, detecting small and coastal ships remains a significant challenge due to the limited features and clutter in coastal environments. For that, a novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers, even in complex environments. Specifically, we first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Then, to filter out the adverse effects of complex backgrounds, we dissect the previously fused multi-level features on the channel, individually excavate the salient regions, and adaptively amalgamate features originating from different channels. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network (FPN). Besides, the AMAM can be readily inserted between different frameworks to improve object detection. Lastly, extensive experiments on two large-scale SAR ship detection datasets demonstrate that our AMANet method is superior to state-of-the-art methods. Comment: 11 pages, 7 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2401.13214 |
| Accession Number: | edsarx.2401.13214 |
| Database: | arXiv |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2401.13214 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20240123&spage=&pages=&title=AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network&atitle=AMANet%3A%20Advancing%20SAR%20Ship%20Detection%20with%20Adaptive%20Multi-Hierarchical%20Attention%20Network&aulast=Ma%2C%20Xiaolin&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
|---|---|
| Header | DbId: edsarx DbLabel: arXiv An: edsarx.2401.13214 RelevancyScore: 1074 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 1074.35656738281 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Xiaolin%22">Ma, Xiaolin</searchLink><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Junkai%22">Cheng, Junkai</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Aihua%22">Li, Aihua</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yuhua%22">Zhang, Yuhua</searchLink><br /><searchLink fieldCode="AR" term="%22Lin%2C+Zhilong%22">Lin, Zhilong</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Computer+Vision+and+Pattern+Recognition%22">Computer Science - Computer Vision and Pattern Recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Artificial+Intelligence%22">Computer Science - Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%2268T45%22">68T45</searchLink><br /><searchLink fieldCode="DE" term="%22I%2E2%2E10%22">I.2.10</searchLink> – Name: Abstract Label: Description Group: Ab Data: Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. Despite the development of numerous ship detection methodologies, detecting small and coastal ships remains a significant challenge due to the limited features and clutter in coastal environments. For that, a novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers, even in complex environments. Specifically, we first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Then, to filter out the adverse effects of complex backgrounds, we dissect the previously fused multi-level features on the channel, individually excavate the salient regions, and adaptively amalgamate features originating from different channels. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network (FPN). Besides, the AMAM can be readily inserted between different frameworks to improve object detection. Lastly, extensive experiments on two large-scale SAR ship detection datasets demonstrate that our AMANet method is superior to state-of-the-art methods.<br />Comment: 11 pages, 7 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2401.13214" linkWindow="_blank">http://arxiv.org/abs/2401.13214</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2401.13214 |
| PLink | https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2401.13214 |
| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Computer Vision and Pattern Recognition Type: general – SubjectFull: Computer Science - Artificial Intelligence Type: general – SubjectFull: Computer Science - Machine Learning Type: general – SubjectFull: 68T45 Type: general – SubjectFull: I.2.10 Type: general Titles: – TitleFull: AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Xiaolin – PersonEntity: Name: NameFull: Cheng, Junkai – PersonEntity: Name: NameFull: Li, Aihua – PersonEntity: Name: NameFull: Zhang, Yuhua – PersonEntity: Name: NameFull: Lin, Zhilong IsPartOfRelationships: – BibEntity: Dates: – D: 23 M: 01 Type: published Y: 2024 |
| ResultId | 1 |