Search

Menu
Search
in Ecology

An efficient method for monitoring small bird targets in wetland environments based on object detection

127 Views


Abstract

Birds play an essential role in evaluating the health and biodiversity of wetland ecosystems. Due to the complex and diverse wetland environments and the typically small size of birds, existing technologies face issues of low detection accuracy and high miss rates. To address these challenges, this study proposes the RLCB-YOLO model, which is a framework for detecting wetland birds based on YOLOv8n. By combining receptive field attention and coordinate attention, the proposed convolutional modules solve the problem of attention weight sharing and enhance long-range information processing. Additionally, the SPPF-LSKA module is introduced to use long-range dependencies and adaptive scaling, effectively filtering background noise in complex wetland environments. For feature fusion, an improved BiFPN-P2 structure is adopted to facilitate superior cross-scale information interaction. The framework is completed by a content-aware feature reorganization module at the up-sampling stage, ensuring precise focus on the key semantic features of small-scale targets. Experimental results showed that RLCB-YOLO achieves 82.1% [email protected] and 48.6% [email protected]:0.95 on a self-built small wetland bird targets dataset, outperforming the baseline YOLOv8n by 3.6% and 2.9%. Furthermore, it outperforms YOLOv8s in overall efficacy while maintaining a reduced parameter count. Visualization analysis further confirms the model’s suitability for engineering applications in ecological monitoring of complex wetland scenes.

Data availability

The data and code used during the current study are available from the corresponding author upon reasonable request.

References

  1. Dawson, T. P., Berry, P. M. & Kampa, E. Climate change impacts on freshwater wetland habitats. J. Nat. Conserv. 11, 25–30. https://doi.org/10.1078/1617-1381-00031 (2003).

    Google Scholar 

  2. Day, J. W. et al. Consequences of climate change on the ecogeomorphology of coastal wetlands. Estuaries coasts. 31, 477–491. https://doi.org/10.1007/s12237-008-9047-6 (2008).

    Google Scholar 

  3. Paracuellos, M. & Tellería, J. L. Factors affecting the distribution of a waterbird community: the role of habitat configuration and bird abundance. Waterbirds 27, 446–453. https://doi.org/10.1675/1524-4695 (2004).

  4. Weller, M. W. Wetland birds: habitat resources and conservation implications (Cambridge University Press, 1999).

    Google Scholar 

  5. Gaston, K. J. et al. Population abundance and ecosystem service provision: the case of birds. BioScience 68, 264–272. https://doi.org/10.1093/biosci/biy005 (2018).

  6. Gregory, R. D., Gibbons, D. W. & Donald, P. F. Bird census and survey techniques (2004).

    Google Scholar 

  7. Du, N., Fathollahi-Fard, A. M. & Wong, K. Y. Wildlife resource conservation and utilization for achieving sustainable development in China: main barriers and problem identification. Environ. Sci. Pollut. Res. 1–20. https://doi.org/10.1007/s11356-023-26982-7 (2023).

  8. Bakó, G., Tolnai, M. & Takács, Á. Introduction and testing of a monitoring and colony-mapping method for waterbird populations that uses high-speed and ultra-detailed aerial remote sensing. Sensors 14, 12828–12846. https://doi.org/10.3390/s140712828 (2014).

    Google Scholar 

  9. Lomnicky, G. A., Herlihy, A. T. & Kaufmann, P. R. Quantifying the extent of human disturbance activities and anthropogenic stressors in wetlands across the conterminous United States: results from the National Wetland Condition Assessment. Environ. Monit. Assess. 191, 324. https://doi.org/10.1007/s10661-019-7314-6 (2019).

    Google Scholar 

  10. Zhang, C. & Lu, Y. Study on artificial intelligence: The state of the art and future prospects. J. Industrial Inform. Integr. 23, 100224. https://doi.org/10.1016/j.jii.2021.100224 (2021).

    Google Scholar 

  11. Pan, Y. Heading toward artificial intelligence 2.0. Engineering 409–413. https://doi.org/10.1016/J.ENG.2016.04.018 (2016).

  12. Yousif, H., Yuan, J., Kays, R. & He, Z. Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images. Ecol. Evol. 9, 1578–1589. https://doi.org/10.1002/ece3.4747 (2019).

    Google Scholar 

  13. Yang, L. et al. Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-020-09486-2 (2021).

    Google Scholar 

  14. Weinstein, B. G. A computer vision for animal ecology. J. Anim. Ecol. 87, 533–545. https://doi.org/10.1111/1365-2656.12780 (2018).

    Google Scholar 

  15. Dang, J., Zhong, Y. & Qin, X. PPformer: Using pixel-wise and patch-wise cross-attention for low-light image enhancement. Comput. Vis. Image Underst. 241, 103930. https://doi.org/10.1016/j.cviu.2024.103930 (2024).

    Google Scholar 

  16. Qin, X. et al. Fourier boundary features network with wider catchers for glass segmentation. IEEE Trans. Image Process. https://doi.org/10.1109/TIP.2025.3592522 (2025).

    Google Scholar 

  17. Li, T. et al. SAM-Guided Semantic Knowledge Fusion for Visible-Infrared Object Detection. Proceedings of the 33rd ACM International Conference on Multimedia, 8835–8844 (2025). https://doi.org/10.1145/3746027.3755718

  18. Guo, Z. et al. Automatic detection of feral pigeons in urban environments using deep learning. Animals 14, 159. https://doi.org/10.3390/ani14010159 (2024).

    Google Scholar 

  19. Takeki, A. et al. Combining deep features for object detection at various scales: finding small birds in landscape images. IPSJ transactions on computer vision and applications 5. https://doi.org/10.1186/s41074-016-0006-z (2016).

  20. Jo, J., Park, J., Han, J., Lee, M. & Smith, A. H. Dynamic bird detection using image processing and neural network. 7th International Conference on Robot Intelligence Technology and Applications (RiTA), 210–214 (2019)., 210–214 (2019). (2019). https://doi.org/10.1109/RITAPP.2019.8932891

  21. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91

  22. Hong, S.-J., Han, Y., Kim, S.-Y., Lee, A.-Y. & Kim, G. Application of deep-learning methods to bird detection using unmanned aerial vehicle imagery. Sensors 19, 1651. https://doi.org/10.3390/s19071651 (2019).

    Google Scholar 

  23. Jiang, T., Zhao, J. & Wang, M. Bird detection on power transmission lines based on improved YOLOv7. Appl. Sci. 13, 11940. https://doi.org/10.3390/app132111940 (2023).

    Google Scholar 

  24. Lei, J. et al. Optimized small waterbird detection method using surveillance videos based on YOLOv7. Animals 13, 1929. https://doi.org/10.3390/ani13121929 (2023).

    Google Scholar 

  25. Chen, X. et al. An efficient method for monitoring birds based on object detection and multi-object tracking networks. Animals 13, 1713. https://doi.org/10.3390/ani13101713 (2023).

    Google Scholar 

  26. Haag, K. H., Lee, T. M. & Water, T. Hydrology and ecology of freshwater wetlands in central Florida: a primer (US Geological Survey, 2010).

    Google Scholar 

  27. Zhang, X. et al. RFAConv: Innovating spatial attention and standard convolutional operation. arXiv preprint arXiv:2304.03198. https://doi.org/10.48550/arXiv.2304.03198 (2023).

  28. Lau, K. W., Po, L.-M. & Rehman, Y. A. U. Large separable kernel attention: Rethinking the large kernel attention design in cnn. Expert Syst. Appl. 236, 121352. https://doi.org/10.1016/j.eswa.2023.121352 (2024).

    Google Scholar 

  29. Liu, S., Qi, L., Qin, H., Shi, J. & Jia, J. Path aggregation network for instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 8759–8768 (2018). https://doi.org/10.1109/CVPR.2018.00913

  30. Lin, T. Y. et al. Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 2117–2125 (2017). https://doi.org/10.1109/CVPR.2017.106

  31. Tan, M., Pang, R., Le, Q. V. & Efficientdet Scalable and efficient object detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10781–10790 (2020). https://doi.org/10.1109/CVPR42600.2020.01079

  32. Wang, J. et al. Carafe: Content-aware reassembly of features. Proceedings of the IEEE/CVF international conference on computer vision, 3007–3016 (2019). https://doi.org/10.1109/ICCV.2019.00310

  33. Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745

  34. Woo, S., Park, J., Lee, J. Y., Kweon, I. S. & Cbam Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), 3–19 (2018). https://doi.org/10.1007/978-3-030-01234-2_1

  35. Chabot, D. & Francis, C. M. Computer-automated bird detection and counts in high‐resolution aerial images: A review. J. Field Ornithol. 87, 343–359. https://doi.org/10.1111/jofo.12171 (2016).

    Google Scholar 

  36. Mesquita, G. P., Rodríguez-Teijeiro, J. D., Wich, S. A. & Mulero-Pázmány, M. Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study. Curr. Zool. 67, 157–163. https://doi.org/10.1093/cz/zoaa038 (2021).

    Google Scholar 

  37. Moll, J. et al. Radar-based Detection of Birds at Wind Turbine Installations: Results from a Field Study. 23rd International Microwave and Radar Conference (MIKON), 285–288 (2020)., 285–288 (2020). (2020). https://doi.org/10.23919/MIKON48703.2020.9253826

  38. Phillips, A. C. et al. Efficacy of avian radar systems for tracking birds on the airfield of a large international airport. Wildl. Soc. Bull. 42, 467–477. https://doi.org/10.1002/wsb.910 (2018).

    Google Scholar 

  39. Creswell, A. et al. Generative adversarial networks: An overview. IEEE. Signal. Process. Mag. 35, 53–65. https://doi.org/10.1109/MSP.2017.2765202 (2018).

  40. Xu, Z., Li, J. & Zhang, M. A surveillance video real-time analysis system based on edge-cloud and fl-yolo cooperation in coal mine. IEEE Access. 9, 68482–68497. https://doi.org/10.1109/ACCESS.2021.3077499 (2021).

    Google Scholar 

  41. Feng, H., Mu, G., Zhong, S., Zhang, P. & Yuan, T. Benchmark analysis of yolo performance on edge intelligence devices. Cryptography 6, 16. https://doi.org/10.3390/cryptography6020016 (2022).

    Google Scholar 

Download references

Funding

This research was funded by the Innovation and Entrepreneurship Education Reform Project of the Ministry of Education of China, grant number 220806340074053 and the Research Project of Wuxi University of Technology, grant number 220125026.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, 150050, China

    Chuanjun Xing

  2. College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China

    Chenpeng Qu

  3. College of Surveying and Mapping Engineering, Heilongjiang Institute of Technology, Harbin, 150050, China

    Pinghao Zhang

  4. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Chengdu College, Chengdu, 610213, China

    Xiaolin Qin

Authors

  1. Chuanjun Xing
    View author publications

    Search author on:PubMed Google Scholar

  2. Chenpeng Qu
    View author publications

    Search author on:PubMed Google Scholar

  3. Pinghao Zhang
    View author publications

    Search author on:PubMed Google Scholar

  4. Xiaolin Qin
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.J.X. and C.P.Q. conceived and designed this paper; C.J.X., C.P.Q. and P.H.Z. performed the experiments and analyzed the data; C.J.X., C.P.Q. and P.H.Z. wrote the paper; C.J.X., C.P.Q.,X.L.Q. and P.H.Z. reviewed and edited the manuscript; C.J.X. provided the funding support. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to
Chenpeng Qu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Xing, C., Qu, C., Zhang, P. et al. An efficient method for monitoring small bird targets in wetland environments based on object detection.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-46593-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-46593-9

Keywords

  • Detection of wetland birds
  • Small object detection
  • YOLOv8
  • Attention mechanisms
  • Separable convolutions
  • Feature integration

Source: Ecology - nature.com

Absolute configuration, improved synthesis and femtogram-level behavioral activity of the sex pheromone of the minute parasitoid wasp Trichogramma turkestanica

A novel approach for disease and pests detection in potato production system based on deep learning

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close