A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images
Abstract Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balan...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92344-7 |
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| author | Shilong Zhou Haijin Zhou Lei Qian |
| author_facet | Shilong Zhou Haijin Zhou Lei Qian |
| author_sort | Shilong Zhou |
| collection | DOAJ |
| description | Abstract Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. To address these challenges, we propose the SMA-YOLO algorithm. First, we introduce the Non-Semantic Sparse Attention (NSSA) mechanism in the backbone network, which efficiently extracts non-semantic features related to the task, thus improving the model’s sensitivity to small objects. In the model’s throat, we design a Bidirectional Multi-Branch Auxiliary Feature Pyramid Network (BIMA-FPN), which integrates high-level semantic information with low-level spatial details, improving small object detection while expanding multi-scale receptive fields. Finally, we incorporate a Channel-Space Feature Fusion Adaptive Head (CSFA-Head), which fully handles multi-scale features and adaptively handles consistency problems of different scales, further improving the robustness of the model in complex scenarios. Experimental results on the VisDrone2019 dataset show that SMA-YOLO achieves a 13% improvement in mAP compared to the baseline model, demonstrating exceptional adaptability in small object detection tasks for remote sensing imagery. These results provide valuable insights and new approaches to further advance research in this area. |
| format | Article |
| id | doaj-art-9cd4c8160dd94d70900c32e6c0078fb6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9cd4c8160dd94d70900c32e6c0078fb62025-08-20T03:41:41ZengNature PortfolioScientific Reports2045-23222025-03-0115111510.1038/s41598-025-92344-7A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing imagesShilong Zhou0Haijin Zhou1Lei Qian2Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of SciencesKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of SciencesKey Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of SciencesAbstract Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. To address these challenges, we propose the SMA-YOLO algorithm. First, we introduce the Non-Semantic Sparse Attention (NSSA) mechanism in the backbone network, which efficiently extracts non-semantic features related to the task, thus improving the model’s sensitivity to small objects. In the model’s throat, we design a Bidirectional Multi-Branch Auxiliary Feature Pyramid Network (BIMA-FPN), which integrates high-level semantic information with low-level spatial details, improving small object detection while expanding multi-scale receptive fields. Finally, we incorporate a Channel-Space Feature Fusion Adaptive Head (CSFA-Head), which fully handles multi-scale features and adaptively handles consistency problems of different scales, further improving the robustness of the model in complex scenarios. Experimental results on the VisDrone2019 dataset show that SMA-YOLO achieves a 13% improvement in mAP compared to the baseline model, demonstrating exceptional adaptability in small object detection tasks for remote sensing imagery. These results provide valuable insights and new approaches to further advance research in this area.https://doi.org/10.1038/s41598-025-92344-7Remote sensing imagesObject detectionMulti-branch auxiliaryFeature fusion |
| spellingShingle | Shilong Zhou Haijin Zhou Lei Qian A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images Scientific Reports Remote sensing images Object detection Multi-branch auxiliary Feature fusion |
| title | A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images |
| title_full | A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images |
| title_fullStr | A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images |
| title_full_unstemmed | A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images |
| title_short | A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images |
| title_sort | multi scale small object detection algorithm sma yolo for uav remote sensing images |
| topic | Remote sensing images Object detection Multi-branch auxiliary Feature fusion |
| url | https://doi.org/10.1038/s41598-025-92344-7 |
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