摘要Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods often overlook the multi-scale nature of bird targets,limiting their ability to capture rich contextual information across different scales.To address this,we propose a cross-dimensional attention network(CDA-Net)for bird detection that integrates spatial and channel information to improve species recognition.The proposed CDA-Net partitions feature maps into multiple channel wise sub-features.Spatial and channel attention are applied to each sub-feature,and the resulting features are fused using the Hadamard product.The fused features are then for-warded to the detection head to generate the final detection results.This approach effectively captures and integrates information across spatial and channel dimensions.Experiments on our self-constructed Nanhai Wetland Waterbird Dataset and the public CUB-200-2011 dataset yield precision scores of 91.32％and 81.99％,respectively,outperforming existing methods.Our approach effectively handles scale variation in bird detection and provides a valuable tool for advancing automated wetland waterbird monitoring.