SheepDA-YOLO: Cross-Domain Adaptive Mean Teacher with Dual-Path Decoupling for Sheep Behavior Recognition

Abstract

With the rapid advancement of smart farming towards large-scale livestock operations, the demand for model generalization in cross-pen behavior recognition has significantly increased. Traditional deep learning models suffer from substantial performance degradation due to variations in illumination and structure across different sheep pens, often necessitating the re-annotation of tens of thousands of frames for each new environment to mitigate domain shift issues. This severely limits the deployment of models in large-scale sheep farms. To achieve the goal of ’annotate once, generalize across pens,’ we propose the SheepDA-YOLO framework, which innovatively integrates contrastive image translation and feature decoupling to address cross-domain adaptation challenges in agriculture. The core of our method consists of four parts: generating bidirectional pseudo-images for source and target domains based on CUT method to reduce image-level domain discrepancies through mixed training sets; employing a Mean Teacher architecture combined with a quadruple loss function to ensure stable knowledge transfer; proposing DP-DMAF module, which suppresses illumination interference and feature confusion through dual-path feature decoupling and separable large-kernel attention, complemented by a high-resolution detection layer to enhance small-target recognition accuracy. Experimental results demonstrate that SheepDA-YOLO achieves 89.7% mAP in cross-domain testing on target sheep pens, outperforming state-of-the-art methods by 3.4% and significantly reducing annotation costs. The study is the first to validate the feasibility of cross-pen adaptation, providing an efficient solution for the scalable implementation of smart livestock farming.