Abstract: 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 forwarded 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.