A Method for Detecting Cattle Behaviors Based on RGB-Depth Dual-Modal Information Fusion.

In large-scale cattle farming, accurate behavior recognition is central to achieving intensive health monitoring and animal welfare assessment. To address challenges such as background interference from fences, feed troughs, and stains in real-world barns, and overlapping of cattle coupled with the inability of single-RGB modalities to capture physical spatial structure, which leads to issues like blurred detection boundaries and significant noise interference-we propose a cattle behavior detection method based on RGB-Depth dual-modal information fusion. This approach jointly models the texture information from RGB images and the spatial structural information from depth images. Within this framework, this paper constructs three collaborative optimization modules: first, the CDSAM module is developed, which evaluates neuron importance through a parameter-free attention mechanism and combines dynamic convolutions to adapt to the cattle's variable postures, effectively suppressing complex background noise. Second, we propose the C2BRA module based on a two-layer routed attention mechanism. By adopting a two-stage modeling approach of "region-level routing-intra-region fine-grained attention," it adapts to changes in target scale and enhances the model's ability to represent spatial context for multi-scale semantic information. Finally, in the prediction stage, a lightweight shared convolutional detection head (LSCD) is introduced. By sharing convolutional parameters across scales and decoupling the classification and regression architectures, it reduces computational overhead while maintaining accuracy. Experimental results show that the improved model achieves a mAP@0.5 of 90.3% on our self-built cattle behavior dataset, representing a 4.3 percentage point increase compared to the baseline model, while reducing GFLOPs from 11.0 G to 9.6 G, a decrease of 12.7%; Visualization results indicate that the improved model can focus more accurately on cattle body contours and key behavioral regions, thereby reducing false negatives and enhancing detection accuracy. Concurrently, the model achieves an optimal balance between detection performance and computational complexity, providing robust technical support for automated cattle behavior monitoring on smart farms.