App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Satellite-based waterbird detection in middle and lower yangtze river wetlands using dynamic convolutional neural networks

Wang Yang1,2
Chen Congxi1
Yuan Zhenyu1
Zhang Chuanlin1
Cao Lixiang1
Zhao Congyu1
Wang Yichen3
1. School of Computer and Information, Anhui Normal University, Wuhu Anhui 241000, China
2. Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. College of Art and Design, Shanghai University of Engineering Science, Shanghai 201620, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0443

Abstract

The wetlands in the middle and lower reaches of the Yangtze River are key habitats for waterbirds along the East Asian-Australasian flyway. To address vegetation occlusion, illumination variation, and poor habitat accessibility in waterbird monitoring within satellite wetlands in this region, this study develops LiteDETR-WetBird, a waterbird detection model based on dynamic convolutional neural networks. The RT-DETR backbone is enhanced with a dynamic separable convolution aggregation module that integrates parallel multi-kernel depthwise convolution with a dynamic kernel-weighting mechanism to reduce computational complexity. A small-object feature enhancement pyramid, combined with spatial depthwise convolution and a spatial–frequency collaborative enhancement path, strengthens the perception of small waterbird targets under complex backgrounds. In addition, a spatial resolution alignment module improves up- and down-sampling and alleviates target information attenuation. Experiments on waterbird datasets collected from six satellite wetlands in Anhui and Jiangsu provinces, together with a generalization study on the CUB_200_2011 dataset, demonstrate that LiteDETR-WetBird reduces the number of parameters by 30.23% while increasing detection precision to 95.37%, outperforming other comparison methods. These results provide effective technical support for ecological monitoring and conservation of satellite wetlands in the middle and lower reaches of the Yangtze River. Experimental code and dataset publicly available: https://github.com/lsy010923/WetBird.

Foundation Support

国家自然科学基金资助项目(32201267)
中国农业农村部区块链农业应用重点实验室开放基金资助项目(2023KLABA04)
智慧农业技术与装备安徽省重点实验室开放基金资助项目(KLAS2023KF001)
芜湖市社会科学研究课题(WHSK202431)
机器视觉安徽省重点实验室开放基金资助项目(KLMVI-2023-HIT-11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0443
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-13] Accepted Paper

Cite This Article

王杨, 陈从喜, 袁振羽, 等. 基于动态卷积神经网络的长江中下游卫星湿地水鸟检测 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0443. (Wang Yang, Chen Congxi, Yuan Zhenyu, et al. Satellite-based waterbird detection in middle and lower yangtze river wetlands using dynamic convolutional neural networks [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0443. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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