Academic Journal
DMA-YOLO: multi-scale object detection method with attention mechanism for aerial images.
| Title: | DMA-YOLO: multi-scale object detection method with attention mechanism for aerial images. |
|---|---|
| Authors: | Li, Ya-ling, Feng, Yong, Zhou, Ming-liang, Xiong, Xian-cai, Wang, Yong-heng, Qiang, Bao-hua |
| Source: | Visual Computer; Jun2024, Vol. 40 Issue 6, p4505-4518, 14p |
| Subject Terms: | Spine, Drone aircraft, Computational complexity |
| Abstract: | Unmanned aerial vehicles are increasingly popular due to their ease of operation, low noise, and portability. However, existing object detection methods perform poorly in detecting small targets in densely arranged, sparsely distributed aerial images. To tackle this issue, we enhanced the general object detection method YOLOv5 and introduced a multi-scale detection method called Detach-Merge Attention YOLO (DMA-YOLO). Specifically, we proposed a Detach-Merge Convolution (DMC) module and embedded it into the backbone network to maximize feature retention. Furthermore, we embedded the Bottleneck Attention Module (BAM) into the detection head to suppress interference from complex background information without significantly increasing computational complexity. To represent and process multi-scale features more effectively, we have integrated an extra detection head and enhanced the neck network into the Bi-directional Feature Pyramid Network (BiFPN) structure. Finally, we adopted the SCYLLA-IoU (SIoU) as a loss function to expedite the convergence rate of our model and enhance the precision of detection results. A series of experiments on the VisDrone2019 and UAVDT datasets have illustrated the effectiveness of DMA-YOLO. Code is available at https://github.com/Yaling-Li/DMA-YOLO. [ABSTRACT FROM AUTHOR] |
| Copyright of Visual Computer is the property of Springer Nature 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: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s00371-023-03095-3 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 177714379 RelevancyScore: 966 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 965.707580566406 |
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| Items | – Name: Title Label: Title Group: Ti Data: DMA-YOLO: multi-scale object detection method with attention mechanism for aerial images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Ya-ling%22">Li, Ya-ling</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Yong%22">Feng, Yong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ming-liang%22">Zhou, Ming-liang</searchLink><br /><searchLink fieldCode="AR" term="%22Xiong%2C+Xian-cai%22">Xiong, Xian-cai</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yong-heng%22">Wang, Yong-heng</searchLink><br /><searchLink fieldCode="AR" term="%22Qiang%2C+Bao-hua%22">Qiang, Bao-hua</searchLink> – Name: TitleSource Label: Source Group: Src Data: Visual Computer; Jun2024, Vol. 40 Issue 6, p4505-4518, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Spine%22">Spine</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unmanned aerial vehicles are increasingly popular due to their ease of operation, low noise, and portability. However, existing object detection methods perform poorly in detecting small targets in densely arranged, sparsely distributed aerial images. To tackle this issue, we enhanced the general object detection method YOLOv5 and introduced a multi-scale detection method called Detach-Merge Attention YOLO (DMA-YOLO). Specifically, we proposed a Detach-Merge Convolution (DMC) module and embedded it into the backbone network to maximize feature retention. Furthermore, we embedded the Bottleneck Attention Module (BAM) into the detection head to suppress interference from complex background information without significantly increasing computational complexity. To represent and process multi-scale features more effectively, we have integrated an extra detection head and enhanced the neck network into the Bi-directional Feature Pyramid Network (BiFPN) structure. Finally, we adopted the SCYLLA-IoU (SIoU) as a loss function to expedite the convergence rate of our model and enhance the precision of detection results. A series of experiments on the VisDrone2019 and UAVDT datasets have illustrated the effectiveness of DMA-YOLO. Code is available at https://github.com/Yaling-Li/DMA-YOLO. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Visual Computer is the property of Springer Nature 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: Identifiers: – Type: doi Value: 10.1007/s00371-023-03095-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 4505 Subjects: – SubjectFull: Spine Type: general – SubjectFull: Drone aircraft Type: general – SubjectFull: Computational complexity Type: general Titles: – TitleFull: DMA-YOLO: multi-scale object detection method with attention mechanism for aerial images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Ya-ling – PersonEntity: Name: NameFull: Feng, Yong – PersonEntity: Name: NameFull: Zhou, Ming-liang – PersonEntity: Name: NameFull: Xiong, Xian-cai – PersonEntity: Name: NameFull: Wang, Yong-heng – PersonEntity: Name: NameFull: Qiang, Bao-hua IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 01782789 Numbering: – Type: volume Value: 40 – Type: issue Value: 6 Titles: – TitleFull: Visual Computer Type: main |
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