ATS-YOLO: A Multi-Scale Small Object Detection Model for Aerial Imagery Based on Context Enhancement and Task Collaboration Mechanisms.
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| Title: | ATS-YOLO: A Multi-Scale Small Object Detection Model for Aerial Imagery Based on Context Enhancement and Task Collaboration Mechanisms. |
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| Authors: | Zhang, Guoyu1 609341993@qq.com, Xu, Yang2 1981@aliyun.com |
| Source: | IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2284-2294. 11p. |
| Subjects: | Drone aircraft, Object recognition (Computer vision), Deep learning, Computer vision, Aerial photographs |
| Abstract: | Unmanned Aerial Vehicles (UAVs) are widely used for applications such as traffic monitoring, smart city management, and disaster response. However, detecting small objects in aerial imagery presents significant challenges, including scale variations, cluttered backgrounds, high object density, and strict computational constraints for onboard deployment. To tackle these issues, we propose ATS-YOLO, a novel multi-scale small object detection framework for aerial imagery that leverages context enhancement and adaptive task collaboration. Our approach includes a lightweight Context-Guided Bilateral Downsampling (CGBD) module that replaces traditional strided convolutions in both the backbone and neck networks. This design minimizes information loss during spatial reduction, preserving essential contextual cues for small object localization. We also introduce a Complementary Multi-Kernel Fusion Module (CMKFM) in the backbone, utilizing a Feature Complementary Mapping (FCM) unit and a Multi-Kernel Perception (MKP) block to enhance feature integration and multi-scale representation learning. By eliminating the P5 detection pyramid level, which provides limited benefits for tiny objects, we streamline the architecture and reduce computational redundancy without sacrificing accuracy. Additionally, our Adaptive Task-Collaborative Detection Head (ATSHead) dynamically balances classification and localization tasks through shared attention mechanisms, enhancing robustness in complex scenarios. Extensive experiments on the VisDrone2019 benchmark show that ATS-YOLO significantly outperforms the baseline YOLOv12s, achieving improvements of 6.0% and 4.7% in mAP0.5 and mAP0.5:0.95, respectively, while reducing model parameters by 40%. In the UAVDT public dataset, we observe gains of 2.8% in mAP0.5 and 1.4% in mAP0.5:0.95, demonstrating substantial performance enhancements. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194196012 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ATS-YOLO: A Multi-Scale Small Object Detection Model for Aerial Imagery Based on Context Enhancement and Task Collaboration Mechanisms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Guoyu%22">Zhang, Guoyu</searchLink><relatesTo>1</relatesTo><i> 609341993@qq.com</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yang%22">Xu, Yang</searchLink><relatesTo>2</relatesTo><i> 1981@aliyun.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2026, Vol. 53 Issue 6, p2284-2294. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photographs%22">Aerial photographs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unmanned Aerial Vehicles (UAVs) are widely used for applications such as traffic monitoring, smart city management, and disaster response. However, detecting small objects in aerial imagery presents significant challenges, including scale variations, cluttered backgrounds, high object density, and strict computational constraints for onboard deployment. To tackle these issues, we propose ATS-YOLO, a novel multi-scale small object detection framework for aerial imagery that leverages context enhancement and adaptive task collaboration. Our approach includes a lightweight Context-Guided Bilateral Downsampling (CGBD) module that replaces traditional strided convolutions in both the backbone and neck networks. This design minimizes information loss during spatial reduction, preserving essential contextual cues for small object localization. We also introduce a Complementary Multi-Kernel Fusion Module (CMKFM) in the backbone, utilizing a Feature Complementary Mapping (FCM) unit and a Multi-Kernel Perception (MKP) block to enhance feature integration and multi-scale representation learning. By eliminating the P5 detection pyramid level, which provides limited benefits for tiny objects, we streamline the architecture and reduce computational redundancy without sacrificing accuracy. Additionally, our Adaptive Task-Collaborative Detection Head (ATSHead) dynamically balances classification and localization tasks through shared attention mechanisms, enhancing robustness in complex scenarios. Extensive experiments on the VisDrone2019 benchmark show that ATS-YOLO significantly outperforms the baseline YOLOv12s, achieving improvements of 6.0% and 4.7% in mAP0.5 and mAP0.5:0.95, respectively, while reducing model parameters by 40%. In the UAVDT public dataset, we observe gains of 2.8% in mAP0.5 and 1.4% in mAP0.5:0.95, demonstrating substantial performance enhancements. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: 11 StartPage: 2284 Subjects: – SubjectFull: Drone aircraft Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Aerial photographs Type: general Titles: – TitleFull: ATS-YOLO: A Multi-Scale Small Object Detection Model for Aerial Imagery Based on Context Enhancement and Task Collaboration Mechanisms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Guoyu – PersonEntity: Name: NameFull: Xu, Yang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 6 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
