YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection.
Saved in:
| Title: | YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection. |
|---|---|
| Authors: | Yuan, Yifei1 (AUTHOR), Wei, Yingmei1,2 (AUTHOR) weiyingmei@nudt.edu.cn, Zhou, Xiaoyan2,3 (AUTHOR), Guo, Yanming1 (AUTHOR), Chen, Jiangming1,2 (AUTHOR), Jiang, Tingshuai1,3 (AUTHOR) |
| Source: | Remote Sensing. Jun2025, Vol. 17 Issue 12, p1989. 29p. |
| Subjects: | Remote sensing, Pyramids, Algorithms, Encoding |
| Abstract: | Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. To address this challenge, we propose a multi-Scale and complex Background Aware network for remote sensing target detection, named YOLO-SBA. Our proposed YOLO-SBA first processes the input through the Multi-Branch Attention Feature Fusion Module (MBAFF) to extract global contextual dependencies and local detail features. It then integrates these features using the Bilateral Attention Feature Mixer (BAFM) for efficient fusion, enhancing the saliency of multi-scale target features to tackle target scale variations. Next, we utilize the Gated Multi-scale Attention Pyramid (GMAP) to perform channel–spatial dual reconstruction and gating fusion encoding on multi-scale feature maps. This enhances target features while finely suppressing spectral redundancy. Additionally, to prevent the loss of effective information extracted by key modules during inference, we improve the downsampling method using Asymmetric Dynamic Downsampling (ADDown), maximizing the retention of image detail information. We achieve the best performance on the DIOR, DOTA, and RSOD datasets. On the DIOR dataset, YOLO-SBA improves mAP by 16.6% and single-category detection AP by 0.8–23.8% compared to the existing state-of-the-art algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 186260793 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Yifei%22">Yuan, Yifei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Yingmei%22">Wei, Yingmei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> weiyingmei@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Xiaoyan%22">Zhou, Xiaoyan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Yanming%22">Guo, Yanming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Jiangming%22">Chen, Jiangming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Tingshuai%22">Jiang, Tingshuai</searchLink><relatesTo>1,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2025, Vol. 17 Issue 12, p1989. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Pyramids%22">Pyramids</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. To address this challenge, we propose a multi-Scale and complex Background Aware network for remote sensing target detection, named YOLO-SBA. Our proposed YOLO-SBA first processes the input through the Multi-Branch Attention Feature Fusion Module (MBAFF) to extract global contextual dependencies and local detail features. It then integrates these features using the Bilateral Attention Feature Mixer (BAFM) for efficient fusion, enhancing the saliency of multi-scale target features to tackle target scale variations. Next, we utilize the Gated Multi-scale Attention Pyramid (GMAP) to perform channel–spatial dual reconstruction and gating fusion encoding on multi-scale feature maps. This enhances target features while finely suppressing spectral redundancy. Additionally, to prevent the loss of effective information extracted by key modules during inference, we improve the downsampling method using Asymmetric Dynamic Downsampling (ADDown), maximizing the retention of image detail information. We achieve the best performance on the DIOR, DOTA, and RSOD datasets. On the DIOR dataset, YOLO-SBA improves mAP by 16.6% and single-category detection AP by 0.8–23.8% compared to the existing state-of-the-art algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=186260793 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17121989 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1989 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Pyramids Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Encoding Type: general Titles: – TitleFull: YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuan, Yifei – PersonEntity: Name: NameFull: Wei, Yingmei – PersonEntity: Name: NameFull: Zhou, Xiaoyan – PersonEntity: Name: NameFull: Guo, Yanming – PersonEntity: Name: NameFull: Chen, Jiangming – PersonEntity: Name: NameFull: Jiang, Tingshuai IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
