Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Mar 18;15(1):9255.
doi: 10.1038/s41598-025-92344-7.

A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images

Shilong Zhou  1 , Haijin Zhou  2 , Lei Qian  1   3
Affiliations

A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images

Shilong Zhou et al. Sci Rep. .

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.

Keywords: Feature fusion; Multi-branch auxiliary; Object detection; Remote sensing images.

PubMed Disclaimer

Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Additional information: The corresponding author is responsible for submitting a competing interests statement on behalf of all authors of the paper. This statement must be included in the submitted article file.

Figures

Fig. 1
Fig. 1
The overall structure of SMA-YOLO.
Fig. 2
Fig. 2
Structure of NSSA, where formula image represents formula image, and formula image and formula image represent H/S and W/S respectively. The figure illustrates the case when the sparse coefficient S is 2, dividing the input features into 4 non-overlapping tensor blocks of different colors. When the sparse coefficient S is 1, it is equivalent to a single block, performing global attention.
Fig. 3
Fig. 3
Structure of semantic and detail fusion modules.
Fig. 4
Fig. 4
The Dynamic Fusion Weights Mechanism of CSFA-Head.
Fig. 5
Fig. 5
The distribution of the objects in the dataset: (a) distribution of the number of classes; (b) the width and height distribution of the target; the concentration of the distribution is indicated by the color gradient from light white to dark blue, indicating that the distribution becomes more and more concentrated.
Fig. 6
Fig. 6
Compare thermal maps generated for different model detection results: (a) Original images. (b) Heat maps of YOLOv8n. (c) Heat maps of SMA-YOLO.
Fig. 7
Fig. 7
The visualization results between SMA-YOLO and YOLOv8n are compared on VisDrone2019, SSDD, and RSOD datasets.
See this image and copyright information in PMC

References

    1. Hasan, A. F. et al. Fractional order extended state observer enhances the performance of controlled tri-copter uav based on active disturbance rejection control. in Mobile Robot: Motion Control and Path Planning, 439–487 (Springer, 2023).
    1. Beg, A., Qureshi, A. R., Sheltami, T. & Yasar, A. UAV-enabled intelligent traffic policing and emergency response handling system for the smart city. Personal Ubiquit. Comput.25, 33–50 (2021). - DOI
    1. Liu, K. & Zheng, J. UAV trajectory optimization for time-constrained data collection in UAV-enabled environmental monitoring systems. IEEE Internet Things J.9, 24300–24314 (2022). - DOI
    1. Yan, Y., Chen, X., Shi, M. & Li, R. A decision support system architecture for intelligent driven unmanned aerial vehicles maritime search and rescue. in 2024 10th international symposium on system security, safety, and reliability (ISSSR), 424–428 (IEEE, 2024).
    1. Wu, J., Wang, H. & Zhang, M. Urban crowd surveillance in an emergency using unmanned air vehicles. J. Guid. Control. Dyn.43, 838–846 (2020). - DOI

LinkOut - more resources