DMFRNet: Dynamic Multi-Scale Feature Reweighting Network for Dairy Cow Detection

Abstract

Simple Summary Accurate dairy cow detection is important for automatic barn monitoring, but it remains challenging because cattle often appear at different scales, overlap with each other, and are captured under uneven illumination. The model addresses three practical challenges: dynamic multi-scale feature extraction is used to represent cattle with different body sizes and poses, parameter-free attention is introduced to enhance discriminative features under occlusion and low-contrast conditions, and a lightweight shared detection head is designed to reduce model complexity. Experimental results show that DMFRNet improves localization accuracy while maintaining a compact model size. This study provides an efficient detection approach for dairy cow monitoring in complex farming environments.