Efficient detection of building in remote sensing images using an improved YOLOv10 network
https://doi.org/10.37661/1816-0301-2025-22-2-33-47
Abstract
Objectives. At present, rapid detection of the location and size of building objects from remote sensing images is important for scientific research value and has practical significance for urban planning, environmental monitoring and disaster management.
Methods. This paper proposes an object detection method based on improved YOLOv10 network, which incorporates Super Token Attention, RepConv and Normalized Weighted Distance to more precisely detect buildings in remote sensing images. This method improves the detection accuracy and efficiency especially for small objects. The LEVIR-CD dataset is used for model training and testing.
Results. The experimental results show that the method demonstrates better accuracy on the building detection task than the traditional YOLOv10 and other methods.
Conclusion. The proposed method significantly enhances the accuracy and efficiency of building detection in remote sensing images
About the Authors
X. Wu
Belarusian State University
Belarus
Belarus
Xianyi Wu, Postgraduate Student of the Faculty of Mechanics and Mathematics
av. Nezavisimosti, 4, Minsk, 220030
Se. V. Ablameyko
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Sergey V. Ablameyko, Acad. of the National Academy of Sciences of Belarus, D. Sc. (Eng.), Prof. of the Faculty of Mechanics and Mathematics
st. Surganova, 6, Minsk, 220012
References
1. Li S. T., Li C. Y., Kang X. D. Current status and future prospects of multi-source remote sensing image fusion. National Remote Sensing Bulletin, 2021, vol. 25, no. 1, pp. 148–166. DOI: 10.11834/jrs.20210259.
2. Luo H. L., Wang W. X., Ye X. Y., Zhu S. X., Bai Y. Q. Research progress on directed object detection based on deep learning. Image and Signal Processing, 2024, vol. 13, no. 3, pp. 258–270. DOI: 10.12677/jisp.2024.133022.
3. Abdikan S., Bilgin G., Sanli F. B., Uslu E., Ustuner M. Enhancing land use classification with fusing dualpolarized terrasar-x and multispectral rapideye data. Journal of Applied Remote Sensing, 2015, vol. 9, no. 1, p. 096054. DOI: 10.1117/1.JRS.9.096054.
4. Liu F. F., Zhu C. M., Zhao N. N., Wu J. H. Remote sensing small target detection based on multimodal fusion. Laser & Optoelectronics Progress, 2024, vol. 61, no. 24, p. 2428010. DOI: 10.3788/LOP241203.
5. Li J., Wei X. M. Research on efficient detection network method for remote sensing images based on selfattention mechanism. Image and Vision Computing, 2024, vol. 142, p. 104884. DOI: 10.1016/j.imavis.2023.104884.
6. Liu D., Zhong L., Wu H., Li S., Li Y. Remote sensing image super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer. Scientific Reports, 2025, vol. 15, p. 2140. DOI: 10.1038/s41598-025-86446-5.
7. Li Z., Wang H., Ma G., Yang W., Ablameyko S. Effective small object detection in remote sensing images based on improved YOLOv8 network. Nonlinear Phenomena in Complex Systems, 2024, vol. 27, no. 3, pp. 278–291. DOI: 10.5281/zenodo.13960639.
8. Tan M., Le Q. V. EfficientNetV2: Smaller models and faster training, 2021. Available at: https://arxiv.org/abs/2104.00298 (accessed 13.02.2025). DOI: 10.48550/arXiv.2104.00298. (Preprint).
9. Woo S., Park J., Lee J. Y., Kweon I. S. CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018. Springer, Cham, 2018, pp. 3–19. DOI: 10.1007/978-3-030-01234-2_1.
10. Wang A., Chen H., Lin Z., Han J., Ding G. RepViT: Revisiting mobile CNN from ViT perspective, 2023. Available at: https://arxiv.org/abs/2307.09283 (accessed 13.02.2025). DOI: 10.48550/arXiv.2307.09283. (Preprint).
11. Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., …, Houlsby N. An image is worth 16×16 words: Transformers for image recognition at scale, 2020. Available at: https://arxiv.org/abs/2010.11929 (accessed 13.02.2025). DOI: 10.48550/arXiv.2010.11929. (Preprint).
12. Wang X., Zhu D., Yan Y. Towards efficient detection for small objects via attention-guided detection network and data augmentation. Sensors, 2022, vol. 22, no. 19, p. 7663. DOI: 10.3390/s22197663.
13. Wang A., Chen H., Liu L. H., Chen K., Lin Z. J., …, Ding G. G. YOLOv10: Real-time end-to-end object detection, 2024. Available at: https://doi.org/10.48550/arXiv.2405.14458 (accessed 13.02.2025). DOI: 10.48550/arXiv.2405.14458. (Preprint).
14. Wang J., Xu C., Yang W., Yu L. A normalized Gaussian Wasserstein distance for tiny object detection, 2021. Available at: https://doi.org/10.48550/arXiv.2110.13389 (accessed 13.02.2025). DOI: 10.48550/arXiv.2110.13389. (Preprint).
15. Huang H. B., Zhou X. Q., Cao J., He R., Tan T. N. Vision transformer with super token sampling, 2022. Available at: https://doi.org/10.48550/arXiv.2211.11167 (accessed 13.02.2025). DOI: 10.48550/arXiv.2211.11167. (Preprint).
16. Wan D. H., Lu R., Tian S., Xu T., Lang X., Ren Z. Mixed local channel attention for object detection. Engineering Applications of Artificial Intelligence, 2023, vol. 123, p. 106442. DOI: 10.1016/j.engappai.2023.106442.
17. Wang H., Ablameyko S. Enhancing small object detection in remote sensing images using mixed local channel attention with YOLOv8. Journal of Computer Technology and Applied Mathematics, 2024, vol. 1, no. 1, pp. 40–45. DOI: 10.5281/zenodo.10986298.
18. Ding X., Zhang X., Ma N., Han J., Ding G., Sun J. RepVGG: Making VGG-style ConvNets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20–25 June 2021, рр. 13 733–13 742.
19. Vasa V. K., Zhu W., Chen X., Qiu P., Dong X., Wang Y. STA-Unet: Rethink the semantic redundant for medical imaging segmentation, 2024. Available at: https://arxiv.org/pdf/2410.11578 (accessed 13.02.2025). DOI: 10.48550/arXiv.2410.11578. (Preprint).
20. Yu Z., Huang H., Chen W., Su Y., Liu Y., Wang X. YOLO-FaceV2: A scale and occlusion aware face detector, 2022. Available at: https://arxiv.org/pdf/2208.02019v2 (accessed 13.02.2025). DOI: 10.48550/arXiv.2208.02019. (Preprint).
21. Chen H., Shi Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sensing, 2020, vol. 12, no. 10, p. 1662. DOI: 10.3390/rs12101662.
Review
For citations:
Wu X., Ablameyko S.V. Efficient detection of building in remote sensing images using an improved YOLOv10 network. Informatics. 2025;22(2):33-47. https://doi.org/10.37661/1816-0301-2025-22-2-33-47
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