Wetbird-DETR: Detection of Waterbirds in Satellite Wetlands of the Middle and Lower Reaches of the Yangtze River, China

The wetlands of the middle and lower reaches of the Yangtze River provide important habitats for migratory waterbirds, but repeated image-based monitoring in small satellite wetlands is difficult because birds are often distant, partly hidden by vegetation, and photographed against complex backgrounds. Here, the use of automated object detection was evaluated as a support tool for waterbird monitoring in these habitats. A lightweight detector, WetBird-DETR, was developed for field images collected in satellite wetlands. The detector was evaluated with a regional dataset from six satellite wetland sites in Anhui and Jiangsu Provinces, China, collected from June 2024 to June 2025 and containing 10 common waterbird species under varied light and background conditions. Supplementary evaluations were also conducted using two public bird datasets. WetBird-DETR maintained competitive detection performance while reducing the number of parameters and computational cost relative to the baseline detector. The results indicate that automated detection can support preliminary image screening, initial localization of birds, and expert review in repeated surveys of satellite wetlands. Broader validation across regions, seasons, habitat types, waterbird communities, and imaging systems will be necessary before the model can be used to support wider ecological inference.

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Publication Details

Journal
Waterbirds
Published
2026-11-02
DOI
https://doi.org/10.1675/063.049.0203
Primary Topic
Advanced Neural Network Applications
Type
article
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article

Wetbird-DETR: Detection of Waterbirds in Satellite Wetlands of the Middle and Lower Reaches of the Yangtze River, China

Congxi Chen, Chuanlin Zhang, Zhenyu Yuan, Yichen Wang et al.
4 links
Waterbirds
Advanced Neural Network Applications
article

Wetbird-DETR: Detection of Waterbirds in Satellite Wetlands of the Middle and Lower Reaches of the Yangtze River, China

Congxi Chen, Chuanlin Zhang, Zhenyu Yuan, Yichen Wang, Yang Wang, Xiang Li
article en

Abstract

The wetlands of the middle and lower reaches of the Yangtze River provide important habitats for migratory waterbirds, but repeated image-based monitoring in small satellite wetlands is difficult because birds are often distant, partly hidden by vegetation, and photographed against complex backgrounds. Here, the use of automated object detection was evaluated as a support tool for waterbird monitoring in these habitats. A lightweight detector, WetBird-DETR, was developed for field images collected in satellite wetlands. The detector was evaluated with a regional dataset from six satellite wetland sites in Anhui and Jiangsu Provinces, China, collected from June 2024 to June 2025 and containing 10 common waterbird species under varied light and background conditions. Supplementary evaluations were also conducted using two public bird datasets. WetBird-DETR maintained competitive detection performance while reducing the number of parameters and computational cost relative to the baseline detector. The results indicate that automated detection can support preliminary image screening, initial localization of birds, and expert review in repeated surveys of satellite wetlands. Broader validation across regions, seasons, habitat types, waterbird communities, and imaging systems will be necessary before the model can be used to support wider ecological inference.

WaterbirdsVol. 49(2)
Shanghai University of Engineering Science (CN), Anhui Normal University (CN)
Clean water and sanitation
Openalex Percentile: Top 19%
Advanced Neural Network Applications
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